Bayesian model selection

Abstract

Model selection is an important problem in many branches including statistical analysis. In this thesis, we use the Bayesian model-selection to find the model that is the most suitable for the given data. We begin with Bayesian inference and theoretical background of Bayesian model selection. We derive the posterior distribution and marginal likelihood for linear regression models. By using Bayesian inference in regression models, we can identify the best model from generated datasets.

Model selection is an important problem in many branches including statistical analysis. In this thesis, we use the Bayesian model-selection to find the model that is the most suitable for the given data. We begin with Bayesian inference and theoretical background of Bayesian model selection. We derive the posterior distribution and marginal likelihood for linear regression models. By using Bayesian inference in regression models, we can identify the best model from generated datasets.