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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.
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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: ===
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226	226	=== 6. Parallel simulations workflow ===
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228		-This example demonstrates **Parameter Space Exploration** (PSE) by running multiple TVB simulations in parallel for different parameter combinations.
	222	+This example demonstrates Parameter Space Exploration (PSE) by running multiple TVB simulations in parallel for different parameter combinations. The workflow uses two nested ForEach components to iterate over coupling and conduction speed sets of values. For each (coupling, conduction_speed) pair, a simulation run is executed with the help of the RunParallelProcess component, which runs the workflow body in separate worker processes (using multiprocessing + dill).
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230		-The workflow uses two nested ForEach components to iterate over coupling and conduction speed sets of values. For each (coupling, conduction_speed) pair, a simulation run is executed with the help of the RunParallelProcess component, which runs the workflow body in separate worker processes (using multiprocessing + dill).
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232	232	[[image:parallel-simulations.png||height="590" width="1100"]]
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