hmm_neuroscience_diffusion: Construct hidden Markov model obtained by discretizing...

In jeremyhengjm/UnbiasedScore: Unbiased estimators for partially observed diffusions

Construct hidden Markov model obtained by discretizing neuroscience diffusion

Description

Define a hidden Markov model obtained by discretizing neuroscience diffusion.

Usage

hmm_neuroscience_diffusion(
  spiketimes1,
  spiketimes2,
  level_observation,
  terminal_time
)

Arguments

spiketimes1	vector specifying cell spike times for the first grid cell
spiketimes2	vector specifying cell spike times for the second grid cell

Value

a list with objects such as: xdimension is the dimension of the latent process; ydimension is the dimension of the observation process; theta_dimension is the dimension of the parameter space; compute_observations compute observation counts given spike times and discretization level; construct_discretization outputs a list containing stepsize, nsteps, statelength and obstimes; construct_successive_discretization outputs lists containing stepsize, nsteps, statelength, obstimes for fine and coarse levels, and coarsetimes of length statelength_fine indexing time steps of coarse level; sigma is the diffusion coefficient of the process; rinit to sample from the initial distribution; rtransition to sample from the Markov transition; dtransition to evaluate the transition density; dmeasurement to evaluate the measurement density; functional is the smoothing function to compute gradient of log-likelihood.

jeremyhengjm/UnbiasedScore documentation built on Nov. 17, 2023, 1:48 a.m.