Both variants can be run as ‘single imputation’ versions, in case the analysis objective is of a purely descriptive nature.īayesian Analysis of Computer Code Output (BACCO) The suggestion is to use this variant, if the missing-data pattern resembles a data fusion situation, or any other missing-by-design pattern, where several variables have identical missing-data patterns. The second variant is also based on PMM, but the focus is on imputing several variables at the same time. The Bayesian Bootstrap allows for generating approximately proper multiple imputations.
The first variant is a variable-by-variable imputation combining sequential regression and Predictive Mean Matching (PMM) that has been extended for unordered categorical data. Included are two variants of Bayesian Bootstrap Predictive Mean Matching to multiply impute missing data. Elementary Bayesian Statistics (bayesian inference on proportions, contingency tables, means and variances, with informative and noninformative priors).īayesian Bootstrap Predictive Mean Matching - Multiple and Single Imputation for Discrete Data Understanding (Central-Limit Theorem and the Normal Distribution, Distribution of a sample mean, Distribution of a sample variance, Probability calculator for common distributions), and II. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.Ī GTK GUI for teaching basic concepts in statistical inference, and doing elementary bayesian testsĪ collection of statistical simulation and computation tools with a GTK GUI, to help teach statistical concepts and compute probabilities. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself.ĭata Analysis Using Regression and Multilevel/Hierarchical Modelsįunctions to accompany A. Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. A comprehensive set of documented case studies, numerical accuracy/quality assurance exercises, and additional documentation are available from the ‘abn’ website.Īdaptive Mixture of Student-t Distributions Laplace approximations are used to estimate goodness of fit metrics and model parameters, and wrappers are also included to the INLA package which can be obtained from. The core functionality is concerned with model selection - determining the most robust empirical model of data from interdependent variables. The usual term to describe this model selection process is structure discovery. The additive formulation of these models is equivalent to multivariate generalised linear modelling (including mixed models with iid random effects). ‘abn’ provides routines to help determine optimal Bayesian network models for a given data set, where these models are used to identify statistical dependencies in messy, complex data.
Additive Bayesian network models are equivalent to Bayesian multivariate regression using graphical modelling, they generalises the usual multivariable regression, GLM, to multiple dependent variables. An additive Bayesian network model consists of a form of a DAG where each node comprises a generalized linear model, GLM. Modelling Multivariate Data with Additive Bayesian Networksīayesian network analysis is a form of probabilistic graphical models which derives from empirical data a directed acyclic graph, DAG, describing the dependency structure between random variables. Cross-validation tools are also available for measuring the accuracy of ABC estimates, and to calculate the misclassification probabilities of different models. Implements several ABC algorithms for performing parameter estimation, model selection, and goodness-of-fit. Tools for Approximate Bayesian Computation (ABC) “Category”, “Package”, “Title” and “Description” are quoted and copied from the above sources./“Category”、 “Package”、 “Title”そして“Description”は上記ソースからコピーしています。