LOGLIKELIHOOD_func: Calculates the Log Likelihood for a new sample given the...

In seeds: Estimate Hidden Inputs using the Dynamic Elastic Net

Description

Algorithm implemented according to Engelhardt et al. 2017. The function can be replaced by an user defined version if necessary.

Usage

LOGLIKELIHOOD_func(
  pars,
  Step,
  OBSERVATIONS,
  x_0,
  parameters,
  EPS_inner,
  INPUT,
  D,
  GIBBS_PAR,
  k,
  MU_JUMP,
  SIGMA_JUMP,
  eps_new,
  objectivfunc
)

Arguments

pars	sampled hidden influence for state k (w_new) at time tn+1
Step	time step of the sample algorithm corresponding to the given vector of time points
OBSERVATIONS	observed values at the given time step/point
x_0	initial values at the given time step/point
parameters	model parameters estimates
EPS_inner	current hidden inputs at time tn
INPUT	discrete input function e.g. stimuli
D	diagonal weight matrix of the current Gibbs step
GIBBS_PAR	GIBBS_PAR[["BETA"]] and GIBBS_PAR[["ALPHA"]]; prespecified or calculated vector of state weights
k	number state corresponding to the given hidden influence (w_new)
MU_JUMP	mean of the normal distributed proposal distribution
SIGMA_JUMP	variance of the normal distributed proposal distribution
eps_new	current sample vector of the hidden influences (including all states)
objectivfunc,	link function to match observations with modeled states

Value

returns the log-likelihood for two given hidden inputs

seeds documentation built on July 14, 2020, 1:07 a.m.