kurtis-s / overture
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#' Turn a non-adaptive Metropolis sampler into an adaptive Metropolis sampler
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#'
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#' Given a non-adpative sampler of the form f(..., s), \code{Amwg} will return a
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#' Given a non-adaptive sampler of the form f(..., s), \code{Amwg} will return a
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#' function g(...) that automatically adapts the Metropolis proposal standard
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#' deviation s to try and acheive a target acceptance rate.
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#' deviation s to try and achieve a target acceptance rate.
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#'
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#' \code{Amwg} uses the Adaptive Metropolis-Within-Gibbs algorithm from Roberts
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#' & Rosenthal (2009), which re-scales the proposal standard deviation after a
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#' fixed number of MCMC iterations have elapsed.  The goal of the algorithm is
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#' to acheive a target acceptance rate for the Metropolis step.  After the
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#' to achieve a target acceptance rate for the Metropolis step.  After the
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#' n\emph{th} batch of MCMC iterations the log of the proposal standard
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#' deviation, \eqn{log(s)}, is increased/decreased by \eqn{\delta(n)}.
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#' \eqn{log(s)} is increased by \eqn{\delta(n)} if the observed acceptance rate
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#' by \eqn{f}.  This functionality can be used, for example, if \eqn{f} samples
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#' each of its returned elements individually, updating each element using a
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#' Metropolis step.  See the examples for an illustration of this use case.  In
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#' such settings, \eqn{f} should be contructed to receive \eqn{s} as a vector
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#' such settings, \eqn{f} should be constructed to receive \eqn{s} as a vector
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#' argument.
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#'
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#' @param f non-adaptive Metropolis sampler of the form f(..., s)
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