# Detail: Hybrid (Conditional Gaussian) Instantiated Model

This page describes **Hybrid (conditional Gaussian)** instantiated models in
the **Instantiated Model** box. These are **mixed discrete/continuous
conditional Gaussian (CG) models fitted to data**, starting from a Hybrid
parametric model.

```{figure} ../../_static/images/tetrad-interface/box-by-box/hybrid-im.png
:name: tetrad-hybrid-im-screenshot
:alt: Hybrid Instantiated Model

Hybrid Instantiated Model
```

A Hybrid instantiated model contains:

- A graph over **discrete and continuous variables** with typed nodes.
- For discrete variables:
  - Estimated probabilities for \(P(X \mid \text{Parents}(X))\).
- For continuous variables:
  - For each configuration of discrete parents, **estimated linear-Gaussian
    regression parameters** (coefficients and variances) conditional on parents.

## How Hybrid instantiated models are created

1. In the **Parametric Model** box, create a **Hybrid (conditional Gaussian)**
   model, making sure that variable types (discrete/continuous) match the data.
2. In the **Estimator** box, select:
   - The Hybrid parametric model, and
   - A mixed dataset from the *Data* box.
3. Choose a Hybrid/CG estimator (when available) and run it.
4. Save or send the fitted result to the **Instantiated Model** box.

## Instantiated Model box layout (Hybrid)

When you select a Hybrid instantiated model, the main panel typically shows:

- For **discrete variables**:
  - Estimated CPTs for their conditional distributions.
- For **continuous variables**:
  - Estimated regression coefficients and error variances, often broken down
    by discrete parent configuration.
- Optional likelihood- or score-based summaries for the overall model.

Because Hybrid models combine discrete and continuous pieces, the instantiated
view often looks like a mix of the Bayes and SEM views.

## Typical uses

Hybrid instantiated models are useful when you want to:

- **Simulate realistic mixed data** from a fitted CG model in the *Simulation*
  box.
- **Compare mixed-model search algorithms** against a known generative Hybrid
  model using the *Compare* box.
- Inspect how continuous variables behave under different discrete parent
  configurations.

## Tips

- Watch sample sizes for each discrete parent configuration; small cell counts
  can lead to unstable continuous-parameter estimates.
- Confirm that variable types and coding (especially for discrete variables)
  are consistent between the data, graph, and parametric model.