# Posts tagged pymc3.HalfNormal

## Bayesian regression with truncated or censored data

The notebook provides an example of how to conduct linear regression when your outcome variable is either censored or truncated.

## Bayesian Additive Regression Trees: Introduction

Bayesian additive regression trees (BART) is a non-parametric regression approach. If we have some covariates \(X\) and we want to use them to model \(Y\), a BART model (omitting the priors) can be represented as:

## Using shared variables (Data container adaptation)

The `pymc.Data` container class wraps the theano shared variable class and lets the model be aware of its inputs and outputs. This allows one to change the value of an observed variable to predict or refit on new data. All variables of this class must be declared inside a model context and specify a name for them.

## GLM: Robust Regression using Custom Likelihood for Outlier Classification

Using PyMC3 for Robust Regression with Outlier Detection using the Hogg 2010 Signal vs Noise method.

## Hierarchical Binomial Model: Rat Tumor Example

This short tutorial demonstrates how to use PyMC3 to do inference for the rat tumour example found in chapter 5 of Bayesian Data Analysis 3rd Edition . Readers should already be familliar with the PyMC3 API.

## Estimating parameters of a distribution from awkwardly binned data

Let us say that we are interested in inferring the properties of a population. This could be anything from the distribution of age, or income, or body mass index, or a whole range of different possible measures. In completing this task, we might often come across the situation where we have multiple datasets, each of which can inform our beliefs about the overall population.

## Multivariate Gaussian Random Walk

This notebook shows how to fit a correlated time series using multivariate Gaussian random walks (GRWs). In particular, we perform a Bayesian regression of the time series data against a model dependent on GRWs.