generate_sample: An implementation of the Metropolis Hasting algorithm
In pauwels2014: Bayesian Experimental Design for Systems Biology.

Description Usage Arguments Details Value Author(s) See Also Examples

This is an implementation of MH algorithm to sample from the posterior distribution. The proposal is a mixture between a gaussian proposal and a single coordinate proposal. The step size is diminished when the rejection rate is too high.

1	generate_sample(theta, knobj, N = 500, step = 1, verbose = F)

`theta`	An initialization parameter named numeric vector
`knobj`	A knowledge list. See `knobjs`.
`N`	The total sample size.
`step`	The proposal distribution expected step length.
`verbose`	Should the sampling process print information about itself?

The posterior is evaluated using eval_log_like_knobj function.

A posterior sample matrix, each row representing a parameter named numeric vector.

Edouard Pauwels

eval_log_like_knobj, knobjs

data(experiment_list1)
data(observables)

## Generate the knowledge object with correct parameter value
knobj <- generate_our_knowledge(transform_params)

## Initialize with some data
knobj$datas[[1]] <- list(
 manip = experiment_list1$nothing,
 data = add_noise(
  simulate_experiment(knobj$global_parameters$true_params_T, knobj, experiment_list1$nothing)[
   knobj$global_parameters$tspan %in% observables[["mrnaLow"]]$reso, 
   observables[["mrnaLow"]]$obs
  ]
 )
)

generate_sample(knobj$global_parameters$params * 50, knobj, N = 10)