---
title: "Bayesian Ridge and ARD Regression in JavaScript"
description: "Run BayesianRidge and sparse ARDRegression in JavaScript or TypeScript with posterior uncertainty and sklearn-style APIs."
canonical_url: "https://ml.kanaries.net/docs/apis/linear/bayesianRegressors"
markdown_url: "https://ml.kanaries.net/docs/apis/linear/bayesianRegressors.md"
---
# Bayesian linear regression in JavaScript

## Algorithm overview

Bayesian linear models place probability distributions over coefficients. They are useful when you want regularized predictions plus uncertainty, or automatic relevance determination that prunes weak features.

## JavaScript implementation

`BayesianRidge` learns a shared coefficient precision and supports `predictStd`. `ARDRegression` learns one precision per feature and zeros coefficients whose precision crosses `thresholdLambda`.

## Interactive Bayesian regression playground

Compare Bayesian Ridge and ARD Regression while changing the data and iteration budget. The live curve and holdout metrics come from the selected `@kanaries/ml` model running in your browser.

> The HTML version includes an interactive Bayesian Regression playground powered by @kanaries/ml. You can change the dataset, noise, and model controls; add observations; and inspect live predictions plus train and holdout metrics. The runnable guide and API reference continue below. [Open the HTML page](https://ml.kanaries.net/docs/apis/linear/bayesianRegressors).

## Quick start

```ts
import { Linear } from '@kanaries/ml';

const X = [[0], [1], [2], [3]];
const y = [1, 3, 5, 7];
const model = new Linear.BayesianRidge();
model.fit(X, y);
const mean = model.predict([[4]]);
const standardDeviation = model.predictStd([[4]]);
console.log({ mean, standardDeviation });
```

## Detailed API reference

Both estimators accept `maxIter`, `tol`, Gamma-prior hyperparameters, and `fitIntercept`, and expose `coef`, `intercept`, `alpha`, `lambda`, `sigma`, and `nIter`.