# I-prior

## Bayesian variable selection for linear models using I-priors

In statistical modelling, there is often a genuine interest to learn the most reasonable, parsimonious, and interpretable model that fits the data. We turn our attention to the problem of variable selection in the context of ordinary linear …

## Regression modelling with I-priors: Applications to functional, multilevel and longitudinal data

We introduce a methodology with the aim of providing a unifying framework for esti- mating a variety of regression methods and models, including multilevel, varying coefficient, longitudinal models, and models with functional covariates and …

## My PhD Project

Regression modelling using priors with Fisher information covariance kernels (I-priors).

## Regression modelling using priors depending on Fisher information covariance kernels (I-priors)

Regression analysis is undoubtedly an important tool to understand the relationship between one or more explanatory and independent variables of interest. In this thesis, we explore a novel methodology for fitting a wide range of parametric and …

## Binary and Multinomial Regression using Fisher Information Covariance Kernels (I-priors)

In a regression setting, we define an I-prior as a Gaussian process prior on the regression function with covariance kernel equal to its Fisher information. We present some methodology and computational work on estimating regression functions by …

## iprior: An R Package for Regression Modelling using I-priors

This is an overview of the R package iprior, which implements a unified methodology for fitting parametric and nonparametric regression models, including additive models, multilevel models, and models with one or more functional covariates. Based on …

## Regression Modelling with I-Priors

This is an overview of a unified methodology for fitting parametric and nonparametric regression models, including additive models, multilevel models, and models with one or more functional covariates. We also discuss an associated R-package called …

## Binary probit regression with I-priors

An extension of the I-prior methodology to binary response data is explored. Starting from a latent variable approach, it is assumed that there exists continuous, auxiliary random variables which decide the outcome of the binary responses. Fitting a …

## R/ipriorBVS

Bayesian Variable Selection for Linear Models using I-priors in `R`.

## R/iprior

An `R` package for I-prior regression.