AdaptiveMultiMRW: AdaptiveMultiMRW

In sourceR: Fits a Non-Parametric Bayesian Source Attribution Model

Description

This class implements an Adaptive Multi-site Metropolis random walk algorithm.

Format

Object of R6Class with methods for updating a Node instance.

Details

A multivariate Gaussian proposal is used, for which the proposal variance is a scaled version of the evolving empirical posterior covariance matrix. See Roberts and Rosenthal (2012) Examples of Adaptive MCMC. Journal of Computational and Graphical Statistics. 18:349–367.

Please note that no checks are performed as to the suitability of this algorithm for a particular StochasticNode. It is up to the user to use the correct update algorithm for the appropriate nodes.

Value

Object of AdaptiveMultiMRW

Fields

cov

the current covariance

burnin

the number of updates to burn in

tune

the current tuning matrix

naccept

the number of accepted proposals

ncalls

the number of times update has been called

node

the node to which the updater is attached

Methods

new(node, tune = rep(0.1, length(node$getData())), burning = 100)

constructor takes an instance of a StochasticNode node, initial tuning vector (diagonal of adaptive tuning matrix), and number of burnin calls.

update()

when called, updates node

acceptance()

return the acceptance rate

sourceR documentation built on Aug. 31, 2020, 5:06 p.m.