Browse Items (9465 total)

BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust
generalization of the popular class of Dirichlet process mixture models. A variety…

Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed
to deal with their dense parameterization, imposing structure on model…

Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting
in TVP models is well known. This issue can be dealt with using…

Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the
large number of latent quantities, their efficient estimation is…

In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with
high-dimensional genomic and other omics data, a problem that can be studied…

Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many
biological studies seek to understand non-deterministic patterns in the…

Generalized additive models (GAMs) are flexible non-linear regression models, which
can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R
package. While the GAM methods provided by mgcv are based on the assumption…

In this study, we present a new module built for users interested in a programming
language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE)
distribution. The module is an extension to the open-source program JAGS by…

ABCpy is a highly modular scientific library for approximate Bayesian computation
(ABC) written in Python. The main contribution of this paper is to document a software
engineering effort that enables domain scientists to easily apply ABC to their…

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model
estimation is complicated by the fact that we typically have multiple…