# Posts by PyMC Contributors

## Out-Of-Sample Predictions

- 26 December 2023

We want to fit a logistic regression model where there is a multiplicative interaction between two numerical features.

## Approximate Bayesian Computation

- 31 May 2022

Approximate Bayesian Computation methods (also called likelihood free inference methods), are a group of techniques developed for inferring posterior distributions in cases where the likelihood function is intractable or costly to evaluate. This does not mean that the likelihood function is not part of the analysis, it just the we are approximating the likelihood, and hence the name of the ABC methods.

## Sequential Monte Carlo

- 19 October 2021

Sampling from distributions with multiple peaks with standard MCMC methods can be difficult, if not impossible, as the Markov chain often gets stuck in either of the minima. A Sequential Monte Carlo sampler (SMC) is a way to ameliorate this problem.

## GLM: Mini-batch ADVI on hierarchical regression model

- 23 September 2021

Unlike Gaussian mixture models, (hierarchical) regression models have independent variables. These variables affect the likelihood function, but are not random variables. When using mini-batch, we should take care of that.

## Marginalized Gaussian Mixture Model

- 18 September 2021

Gaussian mixtures are a flexible class of models for data that exhibits subpopulation heterogeneity. A toy example of such a data set is shown below.