R Under development (unstable) (2024-01-17 r85813 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(testthat) > library(StanEstimators) > > test_check("StanEstimators") Gradient evaluation took 0.000719 seconds 1000 transitions using 10 leapfrog steps per transition would take 7.19 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 100 / 2000 [ 5%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 300 / 2000 [ 15%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 500 / 2000 [ 25%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 700 / 2000 [ 35%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 900 / 2000 [ 45%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1100 / 2000 [ 55%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1300 / 2000 [ 65%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1500 / 2000 [ 75%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1700 / 2000 [ 85%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 1900 / 2000 [ 95%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 3.759 seconds (Warm-up) 3.791 seconds (Sampling) 7.55 seconds (Total) Gradient evaluation took 0.000143 seconds 1000 transitions using 10 leapfrog steps per transition would take 1.43 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 100 / 2000 [ 5%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 300 / 2000 [ 15%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 500 / 2000 [ 25%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 700 / 2000 [ 35%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 900 / 2000 [ 45%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1100 / 2000 [ 55%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1300 / 2000 [ 65%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1500 / 2000 [ 75%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1700 / 2000 [ 85%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 1900 / 2000 [ 95%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.426 seconds (Warm-up) 0.428 seconds (Sampling) 0.854 seconds (Total) Gradient evaluation took 8.7e-05 seconds 1000 transitions using 10 leapfrog steps per transition would take 0.87 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 100 / 2000 [ 5%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 300 / 2000 [ 15%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 500 / 2000 [ 25%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 700 / 2000 [ 35%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 900 / 2000 [ 45%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1100 / 2000 [ 55%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1300 / 2000 [ 65%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1500 / 2000 [ 75%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1700 / 2000 [ 85%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 1900 / 2000 [ 95%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.44 seconds (Warm-up) 0.477 seconds (Sampling) 0.917 seconds (Total) Chain 1: Gradient evaluation took 0.000905 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 9.05 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 1: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 1: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 1: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 1: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 1: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 1: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 1: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 1: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 1: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 1: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 1: Iteration: 2000 / 2000 [100%] (Sampling) Chain 1: Elapsed Time: 3.991 seconds (Warm-up) Chain 1: 3.911 seconds (Sampling) Chain 1: 7.902 seconds (Total) Chain 1: Gradient evaluation took 0.000891 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 8.91 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 2: Gradient evaluation took 0.000957 seconds Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 9.57 seconds. Chain 2: Adjust your expectations accordingly! 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Chain 3: Adjust your expectations accordingly! Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 4: Gradient evaluation took 0.000889 seconds Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 8.89 seconds. Chain 4: Adjust your expectations accordingly! 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Chain 1: Adjust your expectations accordingly! 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Chain 1: Adjust your expectations accordingly! Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 1: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 1: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 2: Gradient evaluation took 0.000139 seconds Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.39 seconds. Chain 2: Adjust your expectations accordingly! Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 2: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 1: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 1: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 2: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 1: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 2: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 1: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 2: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 1: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 2: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 2: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 1: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 1: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 2: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 1: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 2: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 1: Iteration: 2000 / 2000 [100%] (Sampling) Chain 1: Elapsed Time: 0.458 seconds (Warm-up) Chain 1: 0.466 seconds (Sampling) Chain 1: 0.924 seconds (Total) Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 2: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 2: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 2: Iteration: 2000 / 2000 [100%] (Sampling) Chain 2: Elapsed Time: 0.455 seconds (Warm-up) Chain 2: 0.451 seconds (Sampling) Chain 2: 0.906 seconds (Total) Chain 3: Gradient evaluation took 0.000154 seconds Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.54 seconds. Chain 3: Adjust your expectations accordingly! Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 3: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 3: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 3: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 4: Gradient evaluation took 0.000112 seconds Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.12 seconds. Chain 4: Adjust your expectations accordingly! Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 4: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 3: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 3: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 4: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 3: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 4: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 3: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 4: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 4: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 3: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 4: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 3: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 4: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 3: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 3: Iteration: 2000 / 2000 [100%] (Sampling) Chain 3: Elapsed Time: 0.45 seconds (Warm-up) Chain 3: 0.478 seconds (Sampling) Chain 3: 0.928 seconds (Total) Chain 4: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 4: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 4: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 4: Iteration: 2000 / 2000 [100%] (Sampling) Chain 4: Elapsed Time: 0.438 seconds (Warm-up) Chain 4: 0.444 seconds (Sampling) Chain 4: 0.882 seconds (Total) Gradient evaluation took 9.8e-05 seconds 1000 transitions using 10 leapfrog steps per transition would take 0.98 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 100 / 2000 [ 5%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 300 / 2000 [ 15%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 500 / 2000 [ 25%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 700 / 2000 [ 35%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 900 / 2000 [ 45%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1100 / 2000 [ 55%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1300 / 2000 [ 65%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1500 / 2000 [ 75%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1700 / 2000 [ 85%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 1900 / 2000 [ 95%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.484 seconds (Warm-up) 0.514 seconds (Sampling) 0.998 seconds (Total) Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.00068872 0.0176322 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.000688723 0.0176315 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 15 -1072.65 0.000203831 0.0141536 1 1 27 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance ------------------------------------------------------------ EXPERIMENTAL ALGORITHM: This procedure has not been thoroughly tested and may be unstable or buggy. The interface is subject to change. ------------------------------------------------------------ Gradient evaluation took 0.001048 seconds 1000 transitions using 10 leapfrog steps per transition would take 10.48 seconds. Adjust your expectations accordingly! Begin eta adaptation. Iteration: 1 / 250 [ 0%] (Adaptation) Iteration: 50 / 250 [ 20%] (Adaptation) Iteration: 100 / 250 [ 40%] (Adaptation) Iteration: 150 / 250 [ 60%] (Adaptation) Iteration: 200 / 250 [ 80%] (Adaptation) Success! Found best value [eta = 1] earlier than expected. Begin stochastic gradient ascent. iter ELBO delta_ELBO_mean delta_ELBO_med notes 100 -1336.061 1.000 1.000 200 -1079.316 0.619 1.000 300 -1076.062 0.414 0.238 400 -1076.189 0.310 0.238 500 -1076.481 0.248 0.003 MEDIAN ELBO CONVERGED Drawing a sample of size 1000 from the approximate posterior... COMPLETED. ------------------------------------------------------------ EXPERIMENTAL ALGORITHM: This procedure has not been thoroughly tested and may be unstable or buggy. The interface is subject to change. ------------------------------------------------------------ Gradient evaluation took 0.000104 seconds 1000 transitions using 10 leapfrog steps per transition would take 1.04 seconds. Adjust your expectations accordingly! Begin eta adaptation. Iteration: 1 / 250 [ 0%] (Adaptation) Iteration: 50 / 250 [ 20%] (Adaptation) Iteration: 100 / 250 [ 40%] (Adaptation) Iteration: 150 / 250 [ 60%] (Adaptation) Iteration: 200 / 250 [ 80%] (Adaptation) Success! Found best value [eta = 1] earlier than expected. Begin stochastic gradient ascent. iter ELBO delta_ELBO_mean delta_ELBO_med notes 100 -1336.061 1.000 1.000 200 -1079.316 0.619 1.000 300 -1076.062 0.414 0.238 400 -1076.189 0.310 0.238 500 -1076.481 0.248 0.003 MEDIAN ELBO CONVERGED Drawing a sample of size 1000 from the approximate posterior... COMPLETED. ------------------------------------------------------------ EXPERIMENTAL ALGORITHM: This procedure has not been thoroughly tested and may be unstable or buggy. The interface is subject to change. ------------------------------------------------------------ Gradient evaluation took 0.000102 seconds 1000 transitions using 10 leapfrog steps per transition would take 1.02 seconds. Adjust your expectations accordingly! Begin eta adaptation. Iteration: 1 / 250 [ 0%] (Adaptation) Iteration: 50 / 250 [ 20%] (Adaptation) Iteration: 100 / 250 [ 40%] (Adaptation) Iteration: 150 / 250 [ 60%] (Adaptation) Iteration: 200 / 250 [ 80%] (Adaptation) Success! Found best value [eta = 1] earlier than expected. Begin stochastic gradient ascent. iter ELBO delta_ELBO_mean delta_ELBO_med notes 100 -2466.542 1.000 1.000 200 -1179.633 1.045 1.091 300 -1076.472 0.729 1.000 400 -1077.671 0.547 1.000 500 -1080.810 0.438 0.096 600 -1080.691 0.365 0.096 700 -1080.649 0.313 0.003 MEDIAN ELBO CONVERGED Drawing a sample of size 1000 from the approximate posterior... COMPLETED. Path [1] :Initial log joint density = -2306.062377 Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [1] :Best Iter: [14] ELBO (-1075.907817) evaluations: (401) Path [2] :Initial log joint density = -2306.062377 Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [2] :Best Iter: [15] ELBO (-1075.889106) evaluations: (401) Path [3] :Initial log joint density = -2306.062377 Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [3] :Best Iter: [15] ELBO (-1075.912738) evaluations: (401) Path [4] :Initial log joint density = -2306.062377 Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [4] :Best Iter: [14] ELBO (-1075.919504) evaluations: (401) Total log probability function evaluations:5504 Path [1] :Initial log joint density = -2306.062377 Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [1] :Best Iter: [14] ELBO (-1075.907817) evaluations: (401) Path [2] :Initial log joint density = -2306.062377 Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [2] :Best Iter: [15] ELBO (-1075.889106) evaluations: (401) Path [3] :Initial log joint density = -2306.062377 Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [3] :Best Iter: [15] ELBO (-1075.912738) evaluations: (401) Path [4] :Initial log joint density = -2306.062377 Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [4] :Best Iter: [14] ELBO (-1075.919504) evaluations: (401) Total log probability function evaluations:5504 Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.00068872 0.0176322 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Calculating Hessian Calculating inverse of Cholesky factor Generating draws iteration: 0 iteration: 100 iteration: 200 iteration: 300 iteration: 400 iteration: 500 iteration: 600 iteration: 700 iteration: 800 iteration: 900 Calculating Hessian Calculating inverse of Cholesky factor Generating draws iteration: 0 iteration: 100 iteration: 200 iteration: 300 iteration: 400 iteration: 500 iteration: 600 iteration: 700 iteration: 800 iteration: 900 Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.00068872 0.0176322 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Calculating Hessian Calculating inverse of Cholesky factor Generating draws iteration: 0 iteration: 100 iteration: 200 iteration: 300 iteration: 400 iteration: 500 iteration: 600 iteration: 700 iteration: 800 iteration: 900 Gradient evaluation took 0.001393 seconds 1000 transitions using 10 leapfrog steps per transition would take 13.93 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 100 / 2000 [ 5%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 300 / 2000 [ 15%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 500 / 2000 [ 25%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 700 / 2000 [ 35%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 900 / 2000 [ 45%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1100 / 2000 [ 55%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1300 / 2000 [ 65%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1500 / 2000 [ 75%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1700 / 2000 [ 85%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 1900 / 2000 [ 95%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 3.969 seconds (Warm-up) 3.785 seconds (Sampling) 7.754 seconds (Total) Gradient evaluation took 0.000131 seconds 1000 transitions using 10 leapfrog steps per transition would take 1.31 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 100 / 2000 [ 5%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 300 / 2000 [ 15%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 500 / 2000 [ 25%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 700 / 2000 [ 35%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 900 / 2000 [ 45%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1100 / 2000 [ 55%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1300 / 2000 [ 65%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1500 / 2000 [ 75%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1700 / 2000 [ 85%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 1900 / 2000 [ 95%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.516 seconds (Warm-up) 0.486 seconds (Sampling) 1.002 seconds (Total) Gradient evaluation took 0.000153 seconds 1000 transitions using 10 leapfrog steps per transition would take 1.53 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 100 / 2000 [ 5%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 300 / 2000 [ 15%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 500 / 2000 [ 25%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 700 / 2000 [ 35%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 900 / 2000 [ 45%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1100 / 2000 [ 55%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1300 / 2000 [ 65%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1500 / 2000 [ 75%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1700 / 2000 [ 85%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 1900 / 2000 [ 95%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.534 seconds (Warm-up) 0.482 seconds (Sampling) 1.016 seconds (Total) Chain 1: Gradient evaluation took 0.000622 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 6.22 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 1: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 1: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 1: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 1: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 1: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 1: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 1: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 1: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 1: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 1: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 1: Iteration: 2000 / 2000 [100%] (Sampling) Chain 1: Elapsed Time: 3.837 seconds (Warm-up) Chain 1: 3.773 seconds (Sampling) Chain 1: 7.61 seconds (Total) Chain 1: Gradient evaluation took 0.000905 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 9.05 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 2: Gradient evaluation took 0.000764 seconds Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 7.64 seconds. Chain 2: Adjust your expectations accordingly! 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Chain 3: Adjust your expectations accordingly! Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 4: Gradient evaluation took 0.001019 seconds Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 10.19 seconds. Chain 4: Adjust your expectations accordingly! 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Chain 1: Adjust your expectations accordingly! 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Chain 1: Adjust your expectations accordingly! Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 1: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 1: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 2: Gradient evaluation took 0.000106 seconds Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 1.06 seconds. Chain 2: Adjust your expectations accordingly! Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 1: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 2: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 1: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 2: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 1: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 2: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 1: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 2: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 1: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 2: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 1: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 2: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 1: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 2: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 1: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 1: Iteration: 2000 / 2000 [100%] (Sampling) Chain 1: Elapsed Time: 0.46 seconds (Warm-up) Chain 1: 0.406 seconds (Sampling) Chain 1: 0.866 seconds (Total) Chain 2: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 2: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 2: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 2: Iteration: 2000 / 2000 [100%] (Sampling) Chain 2: Elapsed Time: 0.486 seconds (Warm-up) Chain 2: 0.455 seconds (Sampling) Chain 2: 0.941 seconds (Total) Chain 3: Gradient evaluation took 0.00012 seconds Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 1.2 seconds. Chain 3: Adjust your expectations accordingly! Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 3: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 3: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 3: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 3: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 4: Gradient evaluation took 0.00013 seconds Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 1.3 seconds. Chain 4: Adjust your expectations accordingly! Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup) Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 3: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 4: Iteration: 100 / 2000 [ 5%] (Warmup) Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 3: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 4: Iteration: 300 / 2000 [ 15%] (Warmup) Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup) Chain 3: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 4: Iteration: 500 / 2000 [ 25%] (Warmup) Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup) Chain 4: Iteration: 700 / 2000 [ 35%] (Warmup) Chain 3: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 4: Iteration: 900 / 2000 [ 45%] (Warmup) Chain 3: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup) Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling) Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 4: Iteration: 1100 / 2000 [ 55%] (Sampling) Chain 3: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 3: Iteration: 2000 / 2000 [100%] (Sampling) Chain 3: Elapsed Time: 0.384 seconds (Warm-up) Chain 3: 0.394 seconds (Sampling) Chain 3: 0.778 seconds (Total) Chain 4: Iteration: 1300 / 2000 [ 65%] (Sampling) Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 4: Iteration: 1500 / 2000 [ 75%] (Sampling) Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling) Chain 4: Iteration: 1700 / 2000 [ 85%] (Sampling) Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 4: Iteration: 1900 / 2000 [ 95%] (Sampling) Chain 4: Iteration: 2000 / 2000 [100%] (Sampling) Chain 4: Elapsed Time: 0.379 seconds (Warm-up) Chain 4: 0.429 seconds (Sampling) Chain 4: 0.808 seconds (Total) Gradient evaluation took 0.000117 seconds 1000 transitions using 10 leapfrog steps per transition would take 1.17 seconds. Adjust your expectations accordingly! Iteration: 1 / 2000 [ 0%] (Warmup) Iteration: 100 / 2000 [ 5%] (Warmup) Iteration: 200 / 2000 [ 10%] (Warmup) Iteration: 300 / 2000 [ 15%] (Warmup) Iteration: 400 / 2000 [ 20%] (Warmup) Iteration: 500 / 2000 [ 25%] (Warmup) Iteration: 600 / 2000 [ 30%] (Warmup) Iteration: 700 / 2000 [ 35%] (Warmup) Iteration: 800 / 2000 [ 40%] (Warmup) Iteration: 900 / 2000 [ 45%] (Warmup) Iteration: 1000 / 2000 [ 50%] (Warmup) Iteration: 1001 / 2000 [ 50%] (Sampling) Iteration: 1100 / 2000 [ 55%] (Sampling) Iteration: 1200 / 2000 [ 60%] (Sampling) Iteration: 1300 / 2000 [ 65%] (Sampling) Iteration: 1400 / 2000 [ 70%] (Sampling) Iteration: 1500 / 2000 [ 75%] (Sampling) Iteration: 1600 / 2000 [ 80%] (Sampling) Iteration: 1700 / 2000 [ 85%] (Sampling) Iteration: 1800 / 2000 [ 90%] (Sampling) Iteration: 1900 / 2000 [ 95%] (Sampling) Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 0.493 seconds (Warm-up) 0.573 seconds (Sampling) 1.066 seconds (Total) Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.00068872 0.0176322 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.000688723 0.0176315 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 15 -1072.65 0.000203831 0.0141536 1 1 27 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance ------------------------------------------------------------ EXPERIMENTAL ALGORITHM: This procedure has not been thoroughly tested and may be unstable or buggy. The interface is subject to change. ------------------------------------------------------------ Gradient evaluation took 0.001017 seconds 1000 transitions using 10 leapfrog steps per transition would take 10.17 seconds. Adjust your expectations accordingly! Begin eta adaptation. Iteration: 1 / 250 [ 0%] (Adaptation) Iteration: 50 / 250 [ 20%] (Adaptation) Iteration: 100 / 250 [ 40%] (Adaptation) Iteration: 150 / 250 [ 60%] (Adaptation) Iteration: 200 / 250 [ 80%] (Adaptation) Success! Found best value [eta = 1] earlier than expected. Begin stochastic gradient ascent. iter ELBO delta_ELBO_mean delta_ELBO_med notes 100 -1336.061 1.000 1.000 200 -1079.316 0.619 1.000 300 -1076.062 0.414 0.238 400 -1076.189 0.310 0.238 500 -1076.481 0.248 0.003 MEDIAN ELBO CONVERGED Drawing a sample of size 1000 from the approximate posterior... COMPLETED. ------------------------------------------------------------ EXPERIMENTAL ALGORITHM: This procedure has not been thoroughly tested and may be unstable or buggy. The interface is subject to change. ------------------------------------------------------------ Gradient evaluation took 9.6e-05 seconds 1000 transitions using 10 leapfrog steps per transition would take 0.96 seconds. Adjust your expectations accordingly! Begin eta adaptation. Iteration: 1 / 250 [ 0%] (Adaptation) Iteration: 50 / 250 [ 20%] (Adaptation) Iteration: 100 / 250 [ 40%] (Adaptation) Iteration: 150 / 250 [ 60%] (Adaptation) Iteration: 200 / 250 [ 80%] (Adaptation) Success! Found best value [eta = 1] earlier than expected. Begin stochastic gradient ascent. iter ELBO delta_ELBO_mean delta_ELBO_med notes 100 -1336.061 1.000 1.000 200 -1079.316 0.619 1.000 300 -1076.062 0.414 0.238 400 -1076.189 0.310 0.238 500 -1076.481 0.248 0.003 MEDIAN ELBO CONVERGED Drawing a sample of size 1000 from the approximate posterior... COMPLETED. ------------------------------------------------------------ EXPERIMENTAL ALGORITHM: This procedure has not been thoroughly tested and may be unstable or buggy. The interface is subject to change. ------------------------------------------------------------ Gradient evaluation took 8.2e-05 seconds 1000 transitions using 10 leapfrog steps per transition would take 0.82 seconds. Adjust your expectations accordingly! Begin eta adaptation. Iteration: 1 / 250 [ 0%] (Adaptation) Iteration: 50 / 250 [ 20%] (Adaptation) Iteration: 100 / 250 [ 40%] (Adaptation) Iteration: 150 / 250 [ 60%] (Adaptation) Iteration: 200 / 250 [ 80%] (Adaptation) Success! Found best value [eta = 1] earlier than expected. Begin stochastic gradient ascent. iter ELBO delta_ELBO_mean delta_ELBO_med notes 100 -2466.542 1.000 1.000 200 -1179.633 1.045 1.091 300 -1076.472 0.729 1.000 400 -1077.671 0.547 1.000 500 -1080.810 0.438 0.096 600 -1080.691 0.365 0.096 700 -1080.649 0.313 0.003 MEDIAN ELBO CONVERGED Drawing a sample of size 1000 from the approximate posterior... COMPLETED. Path [1] :Initial log joint density = -2306.062377 Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [1] :Best Iter: [14] ELBO (-1075.907817) evaluations: (401) Path [2] :Initial log joint density = -2306.062377 Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [2] :Best Iter: [15] ELBO (-1075.889106) evaluations: (401) Path [3] :Initial log joint density = -2306.062377 Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [3] :Best Iter: [15] ELBO (-1075.912738) evaluations: (401) Path [4] :Initial log joint density = -2306.062377 Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [4] :Best Iter: [14] ELBO (-1075.919504) evaluations: (401) Total log probability function evaluations:5504 Path [1] :Initial log joint density = -2306.062377 Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [1] :Best Iter: [14] ELBO (-1075.907817) evaluations: (401) Path [2] :Initial log joint density = -2306.062377 Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [2] :Best Iter: [15] ELBO (-1075.889106) evaluations: (401) Path [3] :Initial log joint density = -2306.062377 Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [3] :Best Iter: [15] ELBO (-1075.912738) evaluations: (401) Path [4] :Initial log joint density = -2306.062377 Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes 16 -1.072e+03 1.522e-04 6.447e-03 1.000e+00 1.000e+00 401 -1.076e+03 -1.076e+03 Path [4] :Best Iter: [14] ELBO (-1075.919504) evaluations: (401) Total log probability function evaluations:5504 Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.00068872 0.0176322 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Calculating Hessian Calculating inverse of Cholesky factor Generating draws iteration: 0 iteration: 100 iteration: 200 iteration: 300 iteration: 400 iteration: 500 iteration: 600 iteration: 700 iteration: 800 iteration: 900 Calculating Hessian Calculating inverse of Cholesky factor Generating draws iteration: 0 iteration: 100 iteration: 200 iteration: 300 iteration: 400 iteration: 500 iteration: 600 iteration: 700 iteration: 800 iteration: 900 Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.00068872 0.0176322 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Calculating Hessian Calculating inverse of Cholesky factor Generating draws iteration: 0 iteration: 100 iteration: 200 iteration: 300 iteration: 400 iteration: 500 iteration: 600 iteration: 700 iteration: 800 iteration: 900 Initial log joint probability = -2307.67 Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes 14 -1072.65 0.000688723 0.0176315 1 1 24 Optimization terminated normally: Convergence detected: relative gradient magnitude is below tolerance Gradient evaluation took 0.000872 seconds 1000 transitions using 10 leapfrog steps per transition would take 8.72 seconds. Adjust your expectations accordingly! Gradient evaluation took 0.000856 seconds 1000 transitions using 10 leapfrog steps per transition would take 8.56 seconds. Adjust your expectations accordingly! Chain [1] Iteration: 1 / 2000 [ 0%] (Warmup) Chain [1] Iteration: 100 / 2000 [ 5%] (Warmup) Chain [1] Iteration: 200 / 2000 [ 10%] (Warmup) Chain [1] Iteration: 300 / 2000 [ 15%] (Warmup) Chain [1] Iteration: 400 / 2000 [ 20%] (Warmup) Chain [1] Iteration: 500 / 2000 [ 25%] (Warmup) Chain [1] Iteration: 600 / 2000 [ 30%] (Warmup) Chain [1] Iteration: 700 / 2000 [ 35%] (Warmup) Chain [1] Iteration: 800 / 2000 [ 40%] (Warmup) Chain [1] Iteration: 900 / 2000 [ 45%] (Warmup) Chain [1] Iteration: 1000 / 2000 [ 50%] (Warmup) Chain [1] Iteration: 1001 / 2000 [ 50%] (Sampling) Chain [1] Iteration: 1100 / 2000 [ 55%] (Sampling) Chain [1] Iteration: 1200 / 2000 [ 60%] (Sampling) Chain [1] Iteration: 1300 / 2000 [ 65%] (Sampling) Chain [1] Iteration: 1400 / 2000 [ 70%] (Sampling) Chain [1] Iteration: 1500 / 2000 [ 75%] (Sampling) Chain [1] Iteration: 1600 / 2000 [ 80%] (Sampling) Chain [1] Iteration: 1700 / 2000 [ 85%] (Sampling) Chain [1] Iteration: 1800 / 2000 [ 90%] (Sampling) Chain [1] Iteration: 1900 / 2000 [ 95%] (Sampling) Chain [1] Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 4.13 seconds (Warm-up) 3.898 seconds (Sampling) 8.028 seconds (Total) Chain [2] Iteration: 1 / 2000 [ 0%] (Warmup) Chain [2] Iteration: 100 / 2000 [ 5%] (Warmup) Chain [2] Iteration: 200 / 2000 [ 10%] (Warmup) Chain [2] Iteration: 300 / 2000 [ 15%] (Warmup) Chain [2] Iteration: 400 / 2000 [ 20%] (Warmup) Chain [2] Iteration: 500 / 2000 [ 25%] (Warmup) Chain [2] Iteration: 600 / 2000 [ 30%] (Warmup) Chain [2] Iteration: 700 / 2000 [ 35%] (Warmup) Chain [2] Iteration: 800 / 2000 [ 40%] (Warmup) Chain [2] Iteration: 900 / 2000 [ 45%] (Warmup) Chain [2] Iteration: 1000 / 2000 [ 50%] (Warmup) Chain [2] Iteration: 1001 / 2000 [ 50%] (Sampling) Chain [2] Iteration: 1100 / 2000 [ 55%] (Sampling) Chain [2] Iteration: 1200 / 2000 [ 60%] (Sampling) Chain [2] Iteration: 1300 / 2000 [ 65%] (Sampling) Chain [2] Iteration: 1400 / 2000 [ 70%] (Sampling) Chain [2] Iteration: 1500 / 2000 [ 75%] (Sampling) Chain [2] Iteration: 1600 / 2000 [ 80%] (Sampling) Chain [2] Iteration: 1700 / 2000 [ 85%] (Sampling) Chain [2] Iteration: 1800 / 2000 [ 90%] (Sampling) Chain [2] Iteration: 1900 / 2000 [ 95%] (Sampling) Chain [2] Iteration: 2000 / 2000 [100%] (Sampling) Elapsed Time: 3.774 seconds (Warm-up) 3.049 seconds (Sampling) 6.823 seconds (Total) [ FAIL 0 | WARN 0 | SKIP 2 | PASS 40 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • empty test (2): 'test-basic.R:113:1', 'test-captures.R:109:1' [ FAIL 0 | WARN 0 | SKIP 2 | PASS 40 ] > > proc.time() user system elapsed 114.43 5.70 124.43