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          System configuration
          -
          -
          Last updated: Tue Dec 10 2024
          +
           
          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          -IPython   : 8.29.0
          +numpy     : 1.26.0
           jax       : 0.4.31
          -pyhgf     : 0.0.0.post1.dev0+d064162
           seaborn   : 0.13.2
          -numpy     : 1.26.0
          -sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          +pyhgf     : 0.0.0.post1.dev0+304ea45
           matplotlib: 3.9.2
          +sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          +IPython   : 8.29.0
           
           Watermark: 2.5.0
           
          diff --git a/dev/notebooks/1.1-Binary_HGF.html b/dev/notebooks/1.1-Binary_HGF.html index 3ff419c9b..6ea285990 100644 --- a/dev/notebooks/1.1-Binary_HGF.html +++ b/dev/notebooks/1.1-Binary_HGF.html @@ -52,7 +52,7 @@ - + @@ -829,8 +829,8 @@

          Sampling
          NUTS: [tonic_volatility_2]
           
          -
          
          -

          -../_images/92274084c43343457cfbe933888bb1dd2716bf25083d3e45ea9d19e3ddb37e73.png +../_images/7ba29b6a14a4553c45c81e146c610f86085909429b44774236ab54f4f61b22ad.png
          @@ -883,7 +883,7 @@

          Using the learned parameters -../_images/239e94793fde85a8a7b5fa37525fe62f2106aa7bb41aeb50bdad09751ace89ef.png +../_images/95d2c2154e403b320cf3713a1aa6a05386f7dd1da030689430b27a2437e3ec26.png

          @@ -971,8 +971,8 @@

          Sampling#
          NUTS: [tonic_volatility_2, tonic_volatility_3]
           
          -
          
          -
          
          +
          
          +
          
           
          Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 8 seconds.
           
          @@ -990,7 +990,7 @@

          Sampling#

          -../_images/8c687424d28c53a14096bfa3bc810e1bf1a53fb5d072a06832843dcadb9a8aed.png +../_images/89417b1e62f0183ff40decddbec57294f2928d3246744538178ea748b3d124fa.png
          @@ -1028,7 +1028,7 @@

          Using the learned parameters -../_images/6bbb0356a7d2e791451021478051210bca9e8a126f077782093e97fb0f9fecaf.png +../_images/4b377b4ee32d314e234bab8b9ad53cc7881d8c76640c3e0829b9a770044c1b29.png

          @@ -1038,7 +1038,7 @@

          Using the learned parameters -
          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          +pytensor  : 2.25.5
          +matplotlib: 3.9.2
          +pyhgf     : 0.0.0.post1.dev0+304ea45
          +IPython   : 8.29.0
           sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          +numpy     : 1.26.0
           jax       : 0.4.31
          -matplotlib: 3.9.2
           seaborn   : 0.13.2
          -numpy     : 1.26.0
          -pyhgf     : 0.0.0.post1.dev0+d064162
          -pytensor  : 2.25.5
          -IPython   : 8.29.0
           
           Watermark: 2.5.0
           
          diff --git a/dev/notebooks/1.3-Continuous_HGF.html b/dev/notebooks/1.3-Continuous_HGF.html index 59e02b8ca..d8822c2b1 100644 --- a/dev/notebooks/1.3-Continuous_HGF.html +++ b/dev/notebooks/1.3-Continuous_HGF.html @@ -52,7 +52,7 @@ - + @@ -880,8 +880,8 @@

          Sampling
          NUTS: [tonic_volatility_1]
           
          -
          
          -

          @@ -947,7 +947,7 @@

          Using the learned parameters -
          Array(-1106.1298, dtype=float32)
          +
          Array(-1106.1078, dtype=float32)
           
          @@ -1023,8 +1023,8 @@

          Sampling#
          NUTS: [tonic_volatility_1]
           
          -
          
          -
          
          +
          
          +
          
           
          Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 9 seconds.
           
          @@ -1040,7 +1040,7 @@

          Sampling#

          -../_images/5c151dc5ff581ecf21238d061d1c7af058ffdb8cd86e183349a9eb3cef93eb73.png +../_images/d4154620ab0010ad605eae092b608e72e20c8e5133d9a10a0c8804917fe11fb3.png

          @@ -1074,7 +1074,7 @@

          Using the learned parameters -../_images/b9a9de1907f3220c9552221fdfdb27555353584d767cfc1d838f3553532291d3.png +../_images/40601a81b8f83071e14dc3febf12b2d8e7a79e39b9e78fd142a152c743c88317.png

          @@ -1084,7 +1084,7 @@

          Using the learned parameters -
          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          -matplotlib: 3.9.2
          -sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          -jax       : 0.4.31
           IPython   : 8.29.0
           arviz     : 0.20.0
          +matplotlib: 3.9.2
          +jax       : 0.4.31
          +sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
           pymc      : 5.17.0
          -pyhgf     : 0.0.0.post1.dev0+d064162
          +pyhgf     : 0.0.0.post1.dev0+304ea45
           
           Watermark: 2.5.0
           
          diff --git a/dev/notebooks/2-Using_custom_response_functions.html b/dev/notebooks/2-Using_custom_response_functions.html index 0ee11a4e9..1f09c06af 100644 --- a/dev/notebooks/2-Using_custom_response_functions.html +++ b/dev/notebooks/2-Using_custom_response_functions.html @@ -52,7 +52,7 @@ - + @@ -890,9 +890,9 @@

          Recovering HGF parameters from the observed behaviors
          NUTS: [tonic_volatility_2]
           

          -
          
          -
          
          -
          ../_images/efc05b204c793f66864d94895db78fd4d729478d6258d2ddf1bd1f1b44d39c72.png

          The results above indicate that given the responses provided by the participant, the most likely values for the parameter \(\omega_2\) are between -4.9 and -3.1, with a mean at -3.9 (you can find slightly different values if you sample different actions from the decisions function). We can consider this as an excellent estimate given the sparsity of the data, and the complexity of the model.

          @@ -980,24 +980,24 @@

          System configuration

          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          -numpy     : 1.26.0
          -pyhgf     : 0.0.0.post1.dev0+d064162
          -jax       : 0.4.31
          +pyhgf     : 0.0.0.post1.dev0+304ea45
           sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          +IPython   : 8.29.0
          +jax       : 0.4.31
          +arviz     : 0.20.0
          +numpy     : 1.26.0
           matplotlib: 3.9.2
           pymc      : 5.17.0
          -arviz     : 0.20.0
          -IPython   : 8.29.0
           
           Watermark: 2.5.0
           
          diff --git a/dev/notebooks/3-Multilevel_HGF.html b/dev/notebooks/3-Multilevel_HGF.html index e16ed22f6..fcfc252ce 100644 --- a/dev/notebooks/3-Multilevel_HGF.html +++ b/dev/notebooks/3-Multilevel_HGF.html @@ -52,7 +52,7 @@ - + @@ -806,7 +806,7 @@

          Plot the computational graph -../_images/5adbb6e59a7b4be9d4fe6e6dbb81b6a04f44eb22c14261ce8afc8abb6251da7c.svg +../_images/4050fe0347ded05a53df568aec1b49d7910606a6343c0c108276244496910f8b.svg

          @@ -832,12 +832,12 @@

          Sampling
          NUTS: [mu_volatility, sigma_volatility, volatility, mu_temperature, sigma_temperature, inverse_temperature]
           
          -
          
          -
          
          -
          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          +sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          +pyhgf     : 0.0.0.post1.dev0+304ea45
          +numpy     : 1.26.0
          +IPython   : 8.29.0
           pymc      : 5.17.0
          -pytensor  : 2.25.5
           arviz     : 0.20.0
          -numpy     : 1.26.0
           matplotlib: 3.9.2
           seaborn   : 0.13.2
          -pyhgf     : 0.0.0.post1.dev0+d064162
          -IPython   : 8.29.0
          -sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          +pytensor  : 2.25.5
           
           Watermark: 2.5.0
           
          diff --git a/dev/notebooks/4-Parameter_recovery.html b/dev/notebooks/4-Parameter_recovery.html index ad4078110..5ed28862b 100644 --- a/dev/notebooks/4-Parameter_recovery.html +++ b/dev/notebooks/4-Parameter_recovery.html @@ -50,7 +50,7 @@ - + @@ -676,9 +676,9 @@

          Inference from the simulated behaviours
          NUTS: [censored_volatility, inverse_temperature]
           

          -
          
          -
          
          -
          -
          Last updated: Tue Dec 10 2024
          +
           
          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          -jax       : 0.4.31
          -IPython   : 8.29.0
          -pyhgf     : 0.0.0.post1.dev0+d064162
          -matplotlib: 3.9.2
           sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
           numpy     : 1.26.0
           seaborn   : 0.13.2
          +jax       : 0.4.31
          +pyhgf     : 0.0.0.post1.dev0+304ea45
          +IPython   : 8.29.0
          +matplotlib: 3.9.2
           
           Watermark: 2.5.0
           
          diff --git a/dev/notebooks/Example_1_Heart_rate_variability.html b/dev/notebooks/Example_1_Heart_rate_variability.html index f56252b63..65a761998 100644 --- a/dev/notebooks/Example_1_Heart_rate_variability.html +++ b/dev/notebooks/Example_1_Heart_rate_variability.html @@ -50,7 +50,7 @@ - + @@ -574,16 +574,16 @@

          Loading and preprocessing physiological recording
          Downloading ECG channel:   0%|          | 0/2 [00:00<?, ?it/s]
           

          -
          Downloading ECG channel:  50%|█████     | 1/2 [00:00<00:00,  1.05it/s]
          +
          Downloading ECG channel:  50%|█████     | 1/2 [00:01<00:01,  1.11s/it]
           
          -
          Downloading Respiration channel:  50%|█████     | 1/2 [00:00<00:00,  1.05it/s]
          +
          Downloading Respiration channel:  50%|█████     | 1/2 [00:01<00:01,  1.11s/it]
           
          -
          Downloading Respiration channel: 100%|██████████| 2/2 [00:01<00:00,  1.01it/s]
          +
          Downloading Respiration channel: 100%|██████████| 2/2 [00:02<00:00,  1.03s/it]
           
          -
          -
          Last updated: Tue Dec 10 2024
          +
           
          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          -pyhgf     : 0.0.0.post1.dev0+d064162
          -sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          -IPython   : 8.29.0
          +pyhgf     : 0.0.0.post1.dev0+304ea45
           matplotlib: 3.9.2
          -numpy     : 1.26.0
          +sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
           seaborn   : 0.13.2
          +numpy     : 1.26.0
          +IPython   : 8.29.0
           
           Watermark: 2.5.0
           
          diff --git a/dev/notebooks/Example_3_Multi_armed_bandit.html b/dev/notebooks/Example_3_Multi_armed_bandit.html index 0d519ceab..0bc31c436 100644 --- a/dev/notebooks/Example_3_Multi_armed_bandit.html +++ b/dev/notebooks/Example_3_Multi_armed_bandit.html @@ -52,7 +52,7 @@ - + @@ -1085,12 +1085,9 @@

          Bayesian inference
          NUTS: [omega]
           

          -
          
          -
          
          -
          Sampling 2 chains for 1_000 tune and 1_000 draw iterations (2_000 + 2_000 draws total) took 7 seconds.
          -
          -
          -
          -
          Last updated: Tue Dec 10 2024
          +
           
          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          -IPython   : 8.29.0
          -matplotlib: 3.9.2
           numpy     : 1.26.0
          -seaborn   : 0.13.2
          +pyhgf     : 0.0.0.post1.dev0+304ea45
           pandas    : 2.2.3
          +IPython   : 8.29.0
           sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          -pyhgf     : 0.0.0.post1.dev0+d064162
          +seaborn   : 0.13.2
          +matplotlib: 3.9.2
           
           Watermark: 2.5.0
           
          diff --git a/dev/notebooks/Exercise_2_Bayesian_reinforcement_learning.html b/dev/notebooks/Exercise_2_Bayesian_reinforcement_learning.html index e14433a00..4a83afde7 100644 --- a/dev/notebooks/Exercise_2_Bayesian_reinforcement_learning.html +++ b/dev/notebooks/Exercise_2_Bayesian_reinforcement_learning.html @@ -52,7 +52,7 @@ - + @@ -703,7 +703,7 @@

          Parameters optimization

          -../_images/ecd192b619fcda3b40cdb5f737d2b936691486677eb7a4f82b5cbc7f55367e9e.svg +../_images/7d29572ae423fb4c8d4ea736f01dab651bf0ee09a3d53b5838bcf639ba2eaf01.svg
          -
          
          -
          -../_images/fb30ad50512fa98d7a3ccb9ce3bbeaa964e8971e08bdf749547c02cfcebfaaac.png +../_images/8caf92f5ae0ba940256e664ef5b7334b9747081112bbc44e3616eb436eb84484.png
          -
          
          -

          Assess model fitting, here using leave-one-out cross-validation from the Arviz library.

          @@ -925,12 +925,12 @@

          Rescorla-Wagner{"version_major": 2, "version_minor": 0, "model_id": "5ad1605315cc4ddfbd83fb20533a7f21"}
          /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
          +
          /opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
             self.pid = os.fork()
           
          
          -
          -
          
          -

          We have saved the pointwise log probabilities as a variable, here we simply move this variable to the log_likelihoo field of the idata object, so Arviz knows that this can be used later for model comparison.

          @@ -1150,12 +1147,12 @@

          Three-level HGF
          NUTS: [tonic_volatility_2]
           

          -
          
          -

          The resulting samples show belief trajectories for 10 samples for each model (we are not depicting the biased random here for clarity). The trajectories are highly similar, but we can see that the two and three-level HGF are slightly adjusting their learning rates in a way that was more consistent with the observed behaviours.

          @@ -1479,25 +1476,25 @@

          System configuration

          -
          Last updated: Tue Dec 10 2024
          +
          Last updated: Wed Dec 11 2024
           
           Python implementation: CPython
           Python version       : 3.12.7
           IPython version      : 8.29.0
           
          -pyhgf : 0.0.0.post1.dev0+d064162
          +pyhgf : 0.0.0.post1.dev0+304ea45
           jax   : 0.4.31
           jaxlib: 0.4.31
           
          -matplotlib: 3.9.2
          -pyhgf     : 0.0.0.post1.dev0+d064162
          -pymc      : 5.17.0
          +pyhgf     : 0.0.0.post1.dev0+304ea45
           arviz     : 0.20.0
          -IPython   : 8.29.0
          -seaborn   : 0.13.2
          +pytensor  : 2.25.5
          +matplotlib: 3.9.2
           sys       : 3.12.7 (main, Oct  1 2024, 15:17:55) [GCC 11.4.0]
          +seaborn   : 0.13.2
           numpy     : 1.26.0
          -pytensor  : 2.25.5
          +pymc      : 5.17.0
          +IPython   : 8.29.0
           
           Watermark: 2.5.0
           
          diff --git a/dev/searchindex.js b/dev/searchindex.js index 383d5aef9..5d23b3377 100644 --- a/dev/searchindex.js +++ b/dev/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"": [[80, "exercise1.1"], [80, "exercise1.2"], [80, "exercise1.3"], [80, "exercise1.4"], [80, "exercise1.5"], [80, "exercise1.6"], [81, "exercise2.1"], [81, "exercise2.2"]], "API": [[0, "api"]], "Acknowledgments": [[65, "acknowledgments"]], "Add data": [[70, "add-data"], [70, "id4"], [72, "add-data"], [72, "id3"]], "Adding a drift to the random walk": [[67, "adding-a-drift-to-the-random-walk"]], "Autoregressive processes": [[67, "autoregressive-processes"]], "Bayesian inference": [[79, "bayesian-inference"]], "Beliefs trajectories": [[81, "beliefs-trajectories"]], "Biased random": [[81, "biased-random"]], "Binary nodes": [[0, "binary-nodes"]], "Bivariate normal distribution": [[69, "bivariate-normal-distribution"]], "Categorical nodes": [[0, "categorical-nodes"]], "Continuous nodes": [[0, 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