Fit a Hidden Markov Dirichlet Process Model
DirichletProcessHierarchicalMvnormal2
Create a Hierarchical Dirichlet Mixture of
semi-conjugate Multivariate Normal Distributions
Create a Dirichlet Process object
Generate the posterior function of the Dirichlet function
Update the parameters of the hierarchical Dirichlet process object.
Create a Normal mixing distribution
Dirichlet process mixture of Beta distributions with a Uniform Pareto base measure.
Calculate the posterior mean and quantiles from a Dirichlet process object.
Calculate how well the prior predicts the data.
Calculate the posterior parameters for a conjugate prior.
Calculate the parameters that maximise the penalised likelihood.
GaussianFixedVarianceMixtureCreate
Create a Gaussian Mixing Distribution with fixed variance.
Create a multivariate normal mixing distribution
Draw from the prior distribution
Create a Dirichlet Mixture of the Weibull distribution
DirichletProcessExponential
Create a Dirichlet Mixture of Exponentials
Draw prior clusters and weights from the Dirichlet process
Create a mixing distribution object
Initialise a Dirichlet process object
Create a multivariate normal mixing distribution with semi conjugate prior
Plot the Dirichlet process object
Mixing Distribution Likelihood
DirichletProcessHierarchicalBeta
Create a Hierarchical Dirichlet Mixture of Beta Distributions
Calculate the prior density of a mixing distribution
Create a Dirichlet mixture of multivariate normal distributions.
Create a Dirichlet Mixture of Gaussians
Create a Exponential mixing distribution
Tumour incidences in rats
Generate the prior function of the Dirichlet process
Create a Mixing Object for a hierarchical Beta Dirichlet process object.
HierarchicalMvnormal2Create
Create a Mixing Object for a hierarchical semi-conjugate
Multivariate Normal Dirichlet process object.
Print the Dirichlet process object
Identifies the correct clusters labels, in any dimension,
when cluster parameters and global parameters are matched.
Update the Dirichlet process concentration parameter.
Fit the Dirichlet process object
The Likelihood function of a Dirichlet process object.
weighted_function_generator
Generate a weighted function.
The likelihood of the Dirichlet process object
Generate the posterior clusters of a Dirichlet Process
PriorParametersUpdate.beta
Update the prior parameters of a mixing distribution
A flexible package for fitting Bayesian non-parametric models.
Create a Weibull mixing distribution.
PosteriorDraw.exponential
Draw from the posterior distribution
The Stick Breaking representation of the Dirichlet process.
Update the \(\alpha\) and \(\beta\) parameter of a hidden Markov Dirichlet process model.
Create a Beta mixture with zeros at the boundaries.
Diagnostic plots for dirichletprocess objects
Add burn-in to a dirichletprocess object
Create a generic Dirichlet process hidden Markov Model
Update the cluster parameters of the Dirichlet process.
Change the observations of fitted Dirichlet Process.
Update the component of the Dirichlet process
Predict the cluster labels of some new data.
Create a Beta mixing distribution.
DirichletProcessMvnormal2
Create a Dirichlet mixture of multivariate normal distributions with semi-conjugate prior.
Dirichlet process mixture of the Beta distribution.
DirichletProcessGaussianFixedVariance
Create a Dirichlet Mixture of the Gaussian Distribution with fixed variance.