The initial parameters, representing the place the Metropolis hastings algorithm starts
The likelihood function of the model, typically a pseudo-marginal likelihood estimated using the bootstrap particle filter for the PMMH algorithm
Definition of the log-transition, used when calculating the acceptance ratio This is the probability of moving between parameters according to the proposal distribution Note: When using a symmetric proposal distribution (eg.
Definition of the log-transition, used when calculating the acceptance ratio This is the probability of moving between parameters according to the proposal distribution Note: When using a symmetric proposal distribution (eg. Normal) this cancels in the acceptance ratio
the previous parameter value
the proposed parameter value
Prior distribution for the parameters, with default implementation
Proposal density, to propose new parameters for a model
Use the same step for iterations in a stream
Use the Breeze Markov Chain to generate a process of MetropState Calling .sample(n) on this will create a single site metropolis hastings, proposing parameters only from the initial supplied parameter values
A single step of the metropolis hastings algorithm to be used with breeze implementation of Markov Chain.
A single step of the metropolis hastings algorithm to be used with breeze implementation of Markov Chain. This is a slight alteration to the implementation in breeze, here MetropState holds on to the previous calculated pseudo marginal log-likelihood value so we don't need to run the previous particle filter again each iteration