- Referenced in 234 articles
- Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization...
- Referenced in 160 articles
- tsbridge: Calculate normalising constants for Bayesian time series models. The tsbridge package contains a collection ... functions that can be used to estimate normalising constants using the bridge sampler of Meng ... variety of time series Bayesian models, where parameters are estimated using BUGS, and models themselves...
- Referenced in 22 articles
- Mixed Models. This package implements maximum-likelihood estimation in the logistic regression with both binary ... Poisson regression (log-linear model). Secondly, Bayesian estimation based on MCMC in the logistic...
- Referenced in 136 articles
- full Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood estimation with...
- Referenced in 57 articles
- SPSS® Amos enables you to specify, estimate, assess and present models to show hypothesized relationships ... confirm and refine models. Uses Bayesian analysis—to improve estimates of model parameters. Offers various...
- Referenced in 46 articles
- network structure learning, parameter learning and inference. Bayesian network structure learning, parameter learning and inference ... well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries...
- Referenced in 27 articles
- observations. IBAL also integrates Bayesian parameter estimation and decisiontheoretic utility maximization thoroughly into the framework...
- Referenced in 15 articles
- MCMC Outputs of Mixture Models. The Bayesian estimation of mixture models (and more general hidden...
- Referenced in 23 articles
- BioBayes, which provides a framework for Bayesian parameter estimation and evidential model ranking over models...
- Referenced in 14 articles
- Models. Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain...
- Referenced in 12 articles
- package rstanarm: Bayesian Applied Regression Modeling via Stan. Estimates pre-compiled regression models using ... Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with...
- Referenced in 53 articles
- PicHunter: Bayesian relevance feedback for image retrieval. This paper describes PicHunter, an image retrieval system ... estimate of the user’s goal image. To accomplish this, PicHunter uses Bayesian learning based ... selections made during a search to estimate the probability associated with each image. These probabilities...
- Referenced in 8 articles
- HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. The diffusion model ... each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power ... diffusion model), which allows fast and flexible estimation of the the drift-diffusion model ... Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation...
- Referenced in 10 articles
- response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation...
- Referenced in 41 articles
- platform program for Bayesian phylogenetic analysis of molecular sequences. It estimates rooted, time-measured phylogenies...
- Referenced in 9 articles
- version of the package provides the Bayesian estimates...
- Referenced in 43 articles
- Phylogeny. MrBayes is a program for Bayesian inference and model choice across a wide range ... Markov chain Monte Carlo (MCMC) methods to estimate the posterior distribution of model parameters...
- Referenced in 27 articles
- Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models ... state space models, the Kalman filter for estimation and forecasting in dynamic linear models with ... known parameters, and maximum likelihood estimation. It also presents many specific dynamic linear models particularly ... maximum likelihood estimation and a much more elaborated one on Bayesian inference. The last chapter...
- Referenced in 36 articles
- this package to parse, compile, test, estimate, and analyze Stan models by accessing the header ... probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte ... Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization...
- Referenced in 7 articles
- importance or candidate density. The estimation procedure is fully automatic and thus avoids the time ... adaptive mixture procedure through the Bayesian estimation of a mixture of ARCH model fitted...