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⚠️ Early Development — Latents is not yet stable. The API is subject to frequent change.
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  • Getting started
  • User guide
  • API reference
  • Development
  • Examples gallery
  • Benchmarks
  • GitHub
  • PyPI

Section Navigation

  • Group Factor Analysis (GFA)
    • Simulating from the GFA model
    • Fitting GFA to simulated data
    • Posterior distributions and sampling
    • Production workflows
  • Delayed Latents Across Groups (mDLAG)
  • Examples gallery

Examples gallery#

Tutorials demonstrating how to use Latents for latent variable modeling and dimensionality reduction.

Examples are organized by method. Start with the GFA examples to learn the core concepts — the probabilistic hierarchy and workflow patterns apply across all methods.

Group Factor Analysis (GFA)#

These examples demonstrate Group Factor Analysis, a Bayesian method for discovering shared and group-specific latent structure in multi-group data.

GFA uses Automatic Relevance Determination (ARD) to automatically select the appropriate latent dimensionality during inference.

Start with Simulating from the GFA model to understand the generative model, then proceed through fitting and posterior analysis.

Simulating from the GFA model

Simulating from the GFA model

Fitting GFA to simulated data

Fitting GFA to simulated data

Posterior distributions and sampling

Posterior distributions and sampling

Production workflows

Production workflows

Delayed Latents Across Groups (mDLAG)#

These examples will demonstrate mDLAG, a method for analyzing multi-group time series data with temporal delays between groups.

Note

mDLAG is under active development. Examples will be added as the implementation matures.

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Group Factor Analysis (GFA)

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  • Group Factor Analysis (GFA)
  • Delayed Latents Across Groups (mDLAG)

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