mmhData

A list of 2-dimensional Gaussian mixture samples and the corresponding group labels. The samples are generated by 4 different Gaussian distributions. The samples are separated into 30 groups.

datasets

A class of frequently used Bayesian parametric and nonparametric model structures, as well as a set of tools for common analytical tasks. Structures include Gaussian and Normal-Inverse-Wishart conjugate structure, Gaussian and Normal-Inverse-Gamma conjugate structure, Categorical and Dirichlet conjugate structure, Dirichlet Process on positive integers, Dirichlet Process in general, Hierarchical Dirichlet Process ... Tasks include updating posteriors, calculating marginal likelihood, calculating posterior predictive densities, sampling from posterior predictive distributions, calculating "Maximum A Posteriori" (MAP) estimates ... See Murphy (2012, <doi:10.1080/09332480.2014.914768>), Koller and Friedman (2009, <doi:10.1017/s0269888910000275>) and Andrieu, de Freitas, Doucet and Jordan (2003, <doi:10.1023/A:1020281327116>) for more information. See <https://chenhaotian.github.io/Bayesian-Bricks/> to get started.

Haotian Chen

bbricks

Bayesian Methods and Graphical Model Structures for Statistical
Modeling

mmhData function

A list of two elements:<dl class="dl-horizontal">
 <dt>x</dt><dd>matrix, 2-dimensional Gaussian samples, each row is a sample</dd>
 <dt>groupLabel</dt><dd>integer, the group label of each Gaussian sample</dd>
</dl>

Format

Samples from a hierarchical mixture model — mmhData

A list of two elements:<dl class='dl-horizontal'>
 <dt>x</dt><dd>matrix, 2-dimensional Gaussian samples, each row is a sample</dd>
 <dt>groupLabel</dt><dd>integer, the group label of each Gaussian sample</dd>
</dl>

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mmhData: Samples from a hierarchical mixture model

Description

Usage

Arguments

Format