Man pages for pauwels2014
Bayesian Experimental Design for Systems Biology.

active_designSimulates the active design process.
add_infinitesimalFinite difference function
add_noiseNoise generative process for the simulations
armijoPerforms armijo line searcc
BFGS_specialAn implementation of BFGS method for posterior maximization.
compute_gradientFinite difference function
compute_gradient_coordinateFinite difference function
compute_mean_risksCompute an average risk as a function of credit spent
dmvnormGaussian multivariate density
dream6_designSimulates the active design process using the comparison...
estimate_risk_dream6Expected risk estimation (comparison with litterature).
estimate_risk_out_allExpected risk estimation.
eval_kn_log_likeEvaluates the likelihood of a parameter value
eval_log_like_knobjPosterior function.
experiment_list1Molecular perturbations.
expsList of possible experiments
generate_our_knowledgeInitialize a knowledge list.
generate_sampleAn implementation of the Metropolis Hasting algorithm
knobjsKnowledge lists
log_likelihoodUser defined likelihood function.
log_normalizeNormalize in log space
log_priorUser defined log prior
observablesObservable quantities of the model
pauwels2014-packageReproduce numerical experiments
proj_gradLeast square on the positive orthant
random_designSimulates a randim design process.
read_knobjsSummarizes pre-computed results.
reverse_paramsTransform log space parameters back to the original space
risk_theta_funRisk function
risk_theta_vectExpected risk based on a posterior sample
sample_functionGenerates posterior samples
sample_function_multi_mod_weightSample function visiting multiple modes of the posterior
sample_function_single_modSample function visiting a single mode of the posterior.
simulate_experimentSimulates the dynamics of a molecular perturbation
simulate_experiment_no_transformLink to the ode solver.
transform_paramsUser defined parameter transformation function.
pauwels2014 documentation built on May 29, 2017, 9:03 a.m.