pymob.inference package

Contents

pymob.inference package#

Submodules#

pymob.inference.interactive module#

pymob.inference.numpyro_backend module#

pymob.inference.optimization module#

pymob.inference.optimize_indy module#

pymob.inference.pyabc_backend module#

class pymob.inference.pyabc_backend.Posterior(samples)#

Bases: object

draw(i)#
mean()#
to_dict()#
class pymob.inference.pyabc_backend.PyabcBackend(simulation: SimulationBase)#

Bases: object

static array_param_to_1d(name, distribution, dist_param_dict)#
property database#
distance_function_parser()#
property extra_vars#
property history_id#
load_results()#
static map_parameters(theta, parameter_map)#
property max_nr_populations#
property min_eps_diff#
property minimum_epsilon#
property model_id#
model_parser()#
property n_predictions#
static param_to_prior(par)#
plot()#
plot_chains()#
property plot_function#
plot_predictions(data_variable: str, x_dim: str, ax=None, subset={})#
property population_size#
property posterior_coordinates#
property posterior_data_structure#
posterior_predictions(n=50, seed=1)#
prior_parser(free_model_parameters: list)#
property redis_password#
property redis_port#
run()#
property sampler#
store_results()#

results are stored by default in database

pymob.inference.pymoo_backend module#

class pymob.inference.pymoo_backend.PymooBackend(simulation: SimulationBase)#

Bases: object

property cvtol#
distance_function_parser()#
property ftol#
load_results()#
property max_nr_populations#
optimize()#
plot_predictions(data_variable: str, x_dim: str, ax=None, subset={}, upscale_x=True)#
property population_size#
post_processing(pop)#
run()#

Implements the parallelization in pymoo

property seed#
store_results()#
variable_mapper(x)#
variable_parser()#
property verbose#
property xtol#

Module contents#