Rev Language Reference


mvMirrorMultiplier

The adaptive mirror multiplier (normal) proposal of Thawornwattana et al. 2017, uses MCMC samples to find posterior mean and variance on the log-scale. After user-defined waiting time, proposes moves (on the log-scale) on opposite side of posterior mean from current location using a normal distribution with the learned posterior standard deviation (scaled by lambda). Before this time, the move uses mu0 as the mean, and lambda as the standard deviation. WARNING: Disabling tuning disables both tuning of proposal variance and learning of empirical mean and variance. To learn the empirical mean and variance without tuning sigma, set adaptOnly=true.

Usage

mvMirrorMultiplier(Real x, Natural waitBeforeLearning, Natural waitBeforeUsing, Natural maxUpdates, Real mu0, RealPos sigma, Bool tune, Bool adaptOnly, RealPos weight, Probability tuneTarget)

Arguments

x : Real (<stochastic> pass by reference)
The variable on which this move operates.
waitBeforeLearning : Natural (pass by value)
The number of move attempts to wait before tracking the mean and variance of the variable.
Default : 500
waitBeforeUsing : Natural (pass by value)
The number of move attempts to wait before using the learned mean and variance.
Default : 1000
maxUpdates : Natural (pass by value)
The maximum number of updates to the empirical mean and variance.
Default : 10000
mu0 : Real (pass by value)
Initial guess at posterior log-mean.
Default : 0
sigma : RealPos (pass by value)
The tuning parameter, adjusts variance of proposal.
Default : 1
tune : Bool (pass by value)
Should we tune the move during burnin?
Default : TRUE
adaptOnly : Bool (pass by value)
If true, sigma is not tuned but mean and variance are still learned
Default : FALSE
weight : RealPos (pass by value)
The weight determines the relative frequency with which this move will be attempted. For details, see the description of the 'moveschedule' parameter on the documentation page for 'mcmc()'.
Default : 1
tuneTarget : Probability (pass by value)
The acceptance probability targeted by auto-tuning.
Default : 0.44