[PDF][PDF] dirichletprocess: An R package for fitting complex Bayesian nonparametric models
GJ Ross, D Markwick - 2018 - cloud.r-project.org
GJ Ross, D Markwick
2018•cloud.r-project.orgThe dirichletprocess package provides software for creating flexible Dirichlet processes
objects in R. Users can perform nonparametric Bayesian analysis using Dirichlet processes
without the need to program their own inference algorithms. Instead, the user can utilise our
pre-built models or specify their own models whilst allowing the dirichletprocess package to
handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as
building blocks for a variety of statistical models including and not limited to: density …
objects in R. Users can perform nonparametric Bayesian analysis using Dirichlet processes
without the need to program their own inference algorithms. Instead, the user can utilise our
pre-built models or specify their own models whilst allowing the dirichletprocess package to
handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as
building blocks for a variety of statistical models including and not limited to: density …
Abstract
The dirichletprocess package provides software for creating flexible Dirichlet processes objects in R. Users can perform nonparametric Bayesian analysis using Dirichlet processes without the need to program their own inference algorithms. Instead, the user can utilise our pre-built models or specify their own models whilst allowing the dirichletprocess package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models.
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