# Updater Box

```{figure} ../../_static/images/tetrad-interface/box-by-box/updater-box.png
:name: tetrad-updater-box-screenshot
:alt: Updater Box in the Tetrad interface.

Updater Box in the Tetrad interface sidebar and main panel.
```

## Purpose

The **Updater** box is where you apply **updating procedures** to an existing model in a Tetrad project.
It takes an **Instantiated Model (IM)** or an **Estimator** output as input and lets you perform
**probabilistic learning** by specifying:

- **Values for variables** (evidence/observations), and
- **Manipulations** (interventions / do-operations).

Given an instantiated model and those settings, the Updater computes updated (posterior) quantities for
variables of interest.

Currently there are two main families of updaters:

- **Discrete updaters** for **discrete Bayesian models**:
  - Junction Tree Updater (default)
  - Approximate Updater
  - Row Summing Updater
- A **SEM Updater** for **linear Gaussian SEMs**.

## Typical workflow

1. **Connect an instantiated model or estimator**
   - Place an **Instantiated Model** or **Estimator** box on the workbench.
   - Configure it to produce either:
     - A **discrete Bayes model**, or
     - A **linear SEM**.
   - Place an **Updater** box and draw an arrow from the IM/Estimator box to the Updater box.

2. **Open the Updater box**
   - Double-click the Updater box to open its interface.
   - The interface shows:
     - A list of variables from the input model,
     - Fields to set **values** (evidence) for some variables,
     - Controls to mark variables as **manipulated** (intervened on) and assign intervention values,
     - A control for choosing the **updater type** (for discrete models).

3. **Choose an updater type**
   - For **discrete** models, select one of:
     - **Junction Tree Updater** – exact inference using a junction tree (default).
     - **Approximate Updater** – sampling- or approximation-based inference for larger models.
     - **Row Summing Updater** – table-based inference by summing over CPT rows.
   - For **linear SEM** models, the **SEM Updater** is used.

4. **Specify evidence and manipulations**
   - For each variable you want to condition on:
     - Enter an observed value (evidence).
   - For each variable you want to intervene on:
     - Mark it as manipulated and specify the intervention value (e.g., do\(X = x\)).

5. **Run the update**
   - Use the updater’s controls (e.g., an **Update** or **Compute** button, depending on your version)
     to perform inference.
   - The Updater computes:
     - **Posterior distributions** for discrete variables, or
     - **Conditional means and variances** for SEM variables,
     given the specified evidence and manipulations.

6. **Inspect results**
   - Updated probabilities or conditional summaries are shown in the Updater interface.
   - You can adjust values and manipulations and recompute to explore “what-if” scenarios.

## Updater types and detail pages

The Updater box supports the following updater types, depending on the model type:

| Model type              | Updater option          | Detail page                                       |
|-------------------------|-------------------------|---------------------------------------------------|
| Discrete Bayes model    | Junction Tree Updater   | `Tetrad Interface → Junction Tree Updater`        |
| Discrete Bayes model    | Approximate Updater     | `Tetrad Interface → Approximate Updater`          |
| Discrete Bayes model    | Row Summing Updater     | `Tetrad Interface → Row Summing Updater`          |
| Linear SEM              | SEM Updater             | `Tetrad Interface → SEM Updater`                  |

See the individual detail pages for conceptual descriptions and implementation notes for each updater.

## Connecting the Updater with other boxes

The Updater box fits into the broader workflow as follows:

- **Inputs**
  - **Instantiated Model** or **Estimator**:
    - Provides the model to be updated (discrete Bayes model or linear SEM).
  - (Sometimes) **Data**:
    - Data are typically used upstream to estimate the model; the Updater itself works with the instantiated model.

- **Outputs**
  - The Updater does **not** change the model structure or parameters; instead, it:
    - Computes posterior distributions or conditional summaries,
    - Displays them in its own interface.
  - You can use these results to:
    - Interpret effects of interventions,
    - Compare outcomes across different evidence/manipulation scenarios,
    - Inform further modeling decisions.

You can also create **multiple Updater boxes** attached to the same instantiated model to explore different
evidence and intervention scenarios in parallel.

## Common patterns & tips

- Use a **discrete updater** (Junction Tree, Approximate, or Row Summing) when your instantiated model is a
  **discrete Bayes network**.
- Use the **Junction Tree Updater** when exact inference is feasible; switch to the **Approximate Updater**
  when models become too large or dense.
- Use the **Row Summing Updater** when you want a conceptually simple, table-based calculation (useful in
  teaching or debugging).
- Use the **SEM Updater** for **linear Gaussian SEMs** to compute:
  - Conditional means and variances under evidence,
  - The effect of interventions \(\text{do}(X = x)\) in the linear SEM setting.
- When performing “what-if” analyses:
  - Keep the instantiated model fixed,
  - Duplicate an Updater configuration and vary only the evidence or manipulations,
  - Compare the resulting updated quantities.

## Related pages

- `Tetrad Interface → Overview` – high-level tour of the GUI.
- Other boxes that commonly interact with **Updater**:
  - *Instantiated Model* (provides the model to be updated).
  - *Estimator* (produces instantiated models from data).
  - *Data* and *Parametric Model* (used upstream to build the instantiated models).
  - *Simulation* (can generate data for models that are later updated).