Rev Language Reference
srMinESS - Estimated sample size stopping rule
Allow an MCMC run to terminate once the specified criterion has been met.
Usage
Arguments
| minEss : | RealPos (pass by value) |
| The minimum ESS threshold when stopping is allowed. | |
| filename : | String (pass by value) |
| The name of the file containing the samples. | |
| frequency : | Natural (pass by value) |
| The frequency how often to check for convergence. | |
| Default : 10000 | |
| burninMethod : | String (pass by value) |
| Which type of burnin method to use. | |
| Default : ESS | |
| Options : ESS|SEM |
Details
The Effective Sample Size (ESS) is the number of independent samples generated by a MCMC sampler.
The ESS takes into account the correlation between samples within a chain.
Low ESS values represent high autocorrelation in the chain.
If the autocorrelation is higher, then the uncertainty in our estimates is also higher.
The MCMC run will terminate once all parameters in every log file meet the ESS
threshold. As such, performing additional runs will not decrease the number
of generations required to meet the ESS threshold – even though it will increase
the number of indepedent samples in the final, pooled posterior sample.
Example
Binomial example: estimate success probability given 7 successes out of 20 trials
r ~ dnExp(10) p := Probability(ifelse(r < 1, r, 1)) n <- 20 k ~ dnBinomial(n, p) k.clamp(7) mymodel = model(k)
moves = VectorMoves() moves.append( mvSlide(r, delta=0.1, weight=1) )
paramFile = “parameters.log”
monitors = VectorMonitors() monitors.append( mnModel(filename=paramFile, printgen=100, p) )
Stop when all monitored parameters have attained an estimated sample size of 50
stopping_rules[1] = srMinESS(50, file = paramFile, freq = 1000)
Create the MCMC object
mymcmc = mcmc(mymodel, monitors, moves)
Begin the MCMC run
mymcmc.run(rules = stopping_rules)
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