Description Format Details Value Fields Methods

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

Object of `R6Class`

with methods for updating a Node instance.

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.

Object of `AdaptiveMultiMRW`

`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

`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

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