This class implements an Adaptive Multi-site logarithmic Metropolis-Hastings random walk algorithm, constrained so the parameter vector sums to 1.
R6Class with methods for updating a DirichletNode instance.
An adaptive multivariate log-Gaussian proposal is used for $d-1$ elements of a $d$-dimensional parameter
vector contained in
node, with the $d$th element updated to ensure that the vector sums to 1.
This makes the updater useful for Dirichlet distributed random variables, improving on AdaptiveDirMRW by
ensuring proposals do not go negative.
For details of the adaptive scheme, 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.
the current covariance
the number of updates to burn in
the current tuning matrix
the number of accepted proposals
the number of times
update has been called
the node to which the updater is attached
new(node, toupdate = function() 1:length(node$getData()), tune = rep(0.1, length(node$getData())), burning = 100)
constructor takes an instance of a StochasticNode node, function to choose the indices of the elements to update (by default all elements), initial tuning vector (diagonal of adaptive tuning matrix), and number of calls between adaptations.
when called, updates
return the acceptance rate
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