Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -193,11 +193,11 @@
193	193
194	194	=== 2. Full VBI Inference workflow ===
195	195
196		-Contains all VBI components required to run an inference workflow, from __prior sampling__ and __simulation__ to __posterior training__ and __posterior sampling__.
	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	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.
203	203