AdaptMCMC(;ntemp,
          swap=:nonrev,
          algorithm = :ram,
          fulladapt = true,
          acc_sw = 0.234,
          all_levels = false
          )

Create an AdaptMCMC.jl sampler. This sampler uses the AdaptiveMCMC.jl package to sample from the posterior. Namely, this is a parallel tempering algorithm with an adaptive exploration and tempering sampler. For more information please see [https://github.com/mvihola/AdaptiveMCMC.jl].

The arguments of the function are:

• ntemp: Number of temperature to run in parallel tempering
• swap: Which temperature swapping strategy to use, options are:
  • :norev (default) uses a non-reversible tempering scheme (still ergodic)
  • :single single randomly picked swap
  • :randperm swap in random order
  • :sweep upward or downward sweeps picked at random
• algorithm: exploration MCMC algorithm (default is :ram which uses robust adaptive metropolis-hastings) options are:
  • :ram (default) Robust adaptive metropolis
  • :am Adaptive metropolis
  • :asm Adaptive scaling metropolis
  • :aswam Adaptive scaling within adaptive metropolis
• fulladapt: whether we adapt both the tempering ladder and the exploration kernel (default is true, i.e. adapt everything)
• acc_sw: The target acceptance rate for temperature swaps
• all_levels: Store all tempering levels to memory (warning this can use a lot of memory)

sample(post::Posterior, sampler::AdaptMCMC, nsamples, burnin=nsamples÷2, args...; initial_params=nothing, kwargs...)

Sample the posterior post using the AdaptMCMC sampler. This will produce nsamples with the first burnin steps removed. The initial_params indicate where to start the sampler from and it is expected to be a NamedTuple of parameters.

Possible additional kwargs are:

• thin::Int = 1: which says to save only every thin sample to memory
• rng: Specify a random number generator (default uses GLOBAL_RNG)

This return a tuple where:

• First element are the chains from the sampler. If all_levels=false the only the unit temperature (posterior) chain is returned
• Second element is the additional ancilliary information about the samples including the loglikelihood logl, sampler state state, average exploration kernel acceptance rate accexp for each tempering level, and average temperate swap acceptance rates accswp for each tempering level.