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157	157	[[image:timeseries-plotly.png||alt="view3.png" height="593" width="1100"]]
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160		-=== 3. Visualize posterior samples with a pairplot (VBI inferenece) ===
	160	+=== 3. Visualize posterior pairplot resulted from an inference workflow ===
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162		-This functionality is available for the **SamplePosterior** component. Right click on the component and select the **Open Viewer** functionality.
	162	+This functionality is available for the SamplePosterior component. Right click on the component and select the Open Viewer functionality.
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164	164	This opens a new JupyterLab tab with an editable notebook. After following the instructions in the notebook, you will generate a pairplot of the posterior samples, showing the distribution of each parameter (uncertainty) and the relationships between parameters.
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168	168	[[image:plot-posteriors.png||height="593" width="1100"]]
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171		-=== 4. Visualize time series from VBI simulations ===
	171	+=== 4. Visualize time series resulted from VBI simulations ===
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173		-This functionality is available for the **SimulationRunner** component. Right click on the component and select the **Open Viewer** functionality.
	173	+This functionality is available for the SimulationRunner component. Right click on the component and select the Open Viewer functionality.
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175	175	This opens a new JupyterLab tab with an editable notebook. After following the instructions in the notebook, you will see a simple time-series plot of the simulated model signal over time.
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194	194	=== 2. Full VBI Inference workflow ===
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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 (e.g., inference configuration, model, simulator, train posterior, sample posterior).
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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		-
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		-
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.
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204	204	[[image:vbi_workflow.png||height="590" width="1100"]]
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206	206	=== 3. Configuring model parameters using the PhasePlaneWidget: ===