Tools for Nonparametric Martingale Posterior Sampling
Antimode Extractor
Create a CopRe Result ggplot
Create a ggplot of a grideval_result Object
Create a SeqRe Result ggplot
Base Measure for Mixture Models
CopRe Tools for Nonparametric Martingale Posterior Sampling
Copula Resampling
Obtain Functionals from a CopRe Result
Normal-Inverse-Gamma Base Measure for Location-Scale Normal Mixture Mo...
Marginal Gibbs-type Mixture Model Sampler
Grid evaluation of copre_result and seqre_result objects
Length
Mode Extractor
Obtain Moments from a CopRe or SeqRe Result
Create a CopRe Result Plot
Create a Plot of a grideval_result Object
Create a SeqRe Result Plot
Register autoplot methods to ggplot2
Register S3 Methods from External Packages
Sequence Measure for Species Sampling Models
Sequence Resampling
Dirichlet Sequence Measure.
Collapsed Gnedin Process Sequence Measure.
Pitman-Yor Sequence Measure.
Performs Bayesian nonparametric density estimation using Martingale posterior distributions including the Copula Resampling (CopRe) algorithm. Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and an extension to include full uncertainty quantification via a predictive sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate random nonparametric distributions as output, leading to complete nonparametric inference on posterior summaries. Routines for calculating arbitrary functionals from the sampled distributions are included as well as an important algorithm for finding the number and location of modes, which can then be used to estimate the clusters in the data using, for example, k-means. Implements work developed in Moya B., Walker S. G. (2022). <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) <doi:10.1080/01621459.1995.10476550>.