a function from parameters to LogLikelihood
the starting parameters for the metropolis algorithm
a SYMMETRIC proposal distribution for the metropolis algorithm (eg. Gaussian)
the starting parameters for the metropolis algorithm
the starting parameters for the metropolis algorithm
a function from parameters to LogLikelihood
a function from parameters to LogLikelihood
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
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 an alteration to the implementation in breeze, here ParamsState 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
Prior distribution for the parameters, with default implementation
Prior distribution for the parameters, with default implementation
a SYMMETRIC proposal distribution for the metropolis algorithm (eg.
a SYMMETRIC proposal distribution for the metropolis algorithm (eg. Gaussian)
Implementation of the particle metropolis algorithm
a function from parameters to LogLikelihood
the starting parameters for the metropolis algorithm
a SYMMETRIC proposal distribution for the metropolis algorithm (eg. Gaussian)