Changes for page Extension tvb-ext-xircuits
Last modified by teodoramisan on 2026/02/13 10:11
From version 79.1
edited by teodoramisan
on 2026/02/13 10:07
on 2026/02/13 10:07
Change comment:
There is no comment for this version
To version 81.1
edited by teodoramisan
on 2026/02/13 10:11
on 2026/02/13 10:11
Change comment:
There is no comment for this version
Summary
-
Page properties (1 modified, 0 added, 0 removed)
Details
- Page properties
-
- Content
-
... ... @@ -195,11 +195,11 @@ 195 195 196 196 Contains all VBI components required to run an inference workflow, from __prior sampling__ and __simulation__ to __posterior training__ and __posterior sampling__. 197 197 198 -The workflow starts with **ConfigInference**, which builds the configuration inputs needed by the workflow. It samples parameter values from the prior distribution to generate theta and prepares the feature-extraction configuration (cfg) used later in the pipeline. 198 +The workflow starts with **ConfigInference**, which builds the configuration inputs needed by the workflow. It samples parameter values from the prior distribution to generate __theta__ and prepares the feature-extraction configuration (__cfg__) used later in the pipeline. 199 199 200 -Next, **SimulationRunner** executes the selected **VBI model** for a batch of parameter samples (theta) using the chosen backend (//cpp//, //cupy// or //numba//). It selects the requested output signal from the model result and extracts the summary features defined in cfg, producing the feature matrix used for training. 200 +Next, **SimulationRunner** executes the selected **VBI model** for a batch of parameter samples (theta) using the chosen backend (//cpp//, //cupy// or //numba//). It selects the requested output signal from the model result and extracts the summary features defined in cfg, producing the __feature matrix__ used for training. 201 201 202 -The resulting features and parameter samples are then passed to **TrainPosterior**, which standardizes the feature matrix with //StandardScaler //and trains a posterior distribution using an SBI method ( for example//SNPE//, //SNLE//, or //SNRE//). In the last step, **SamplePosterior** draws parameter samples from the trained posterior distribution, conditioned on the selected observed feature vector.202 +The resulting features and parameter samples are then passed to **TrainPosterior**, which standardizes the feature matrix with //StandardScaler //and trains a posterior distribution using an SBI method (//SNPE//, //SNLE//, or //SNRE//). In the last step, **SamplePosterior** draws parameter samples from the trained posterior distribution, conditioned on the selected observed feature vector. 203 203 204 204 [[image:vbi_workflow.png||height="590" width="1100"]] 205 205