polya.sample

A Bayesian Nonparametric model for the study of time-evolving frequencies, which has become renowned in the study of population genetics.
The model consists of a Hidden Markov Model (HMM) in which the latent signal is a distribution-valued stochastic process that takes the form of a finite mixture of Dirichlet Processes, indexed by vectors that count how many times each value is observed in the population.
The package implements methodologies presented in Ascolani, Lijoi and Ruggiero (2021) <doi:10.1214/20-BA1206> and Ascolani, Lijoi and Ruggiero (2023) <doi:10.3150/22-BEJ1504> that make it possible to study the process at the time of data collection or to predict its evolution in future or in the past.

Stefano Damato

FVDDPpkg

Implement Fleming-Viot-Dependent Dirichlet Processes

polya.sample function

<dl><dt>n</dt>
<dd>The amount of samples to be drawn.</dd>
<dt>theta</dt>
<dd>The intensity, in the sense of Bayesian Statistics</dd>
<dt>v</dt>
<dd>A vector of values, considered to be already drawn from the Polya scheme.</dd>
<dt>sampling.f</dt>
<dd>A function to sample new values. Its unique argument must express the number of values to draw.</dd></dl>

Arguments

Sampling via Polya Urn scheme — polya.sample

<dl>

<dt>n</dt>
<dd>The amount of samples to be drawn.</dd>


<dt>theta</dt>
<dd>The intensity, in the sense of Bayesian Statistics</dd>


<dt>v</dt>
<dd>A vector of values, considered to be already drawn from the Polya scheme.</dd>


<dt>sampling.f</dt>
<dd>A function to sample new values. Its unique argument must express the number of values to draw.</dd>

</dl>

polya.sample: Sampling via Polya Urn scheme

Description

Usage

Value

Arguments

Examples