optbayesexpt API¶
OptBayesExpt
is the core class that performs Bayesian inference and selects measurement settings. Typically, it is the only class that a user will need to interact with directly.OptBayesExptNoiseParameter
is similar toOptBayesExpt
but is designed for cases where the measurement uncertainty is a parameter to be estimated.ParticlePDF
is inherited byOptBayesExpt
to handle the duties of a probability distribution functions.OBE_Server
class provides communication with other processes through a mini-language of label-value commands.Socket
class is inherited byServer
to handle TCP connections and message encoding/decoding.The obe_utils.py file provides
A
MeasurementSimulator
class that uses “true value” parameters and added noise to simulate experimental outputs.For post-processing, a
trace_sort()
function sorts measurement data by measurement setting and combines all measurements with settings in common.A
differential_entropy()
function to calculate information entropy from samples of a distribution.
- OptBayesExpt class
OptBayesExpt
N_DRAWS
allsettings
choke
cons
cost_estimate()
default_noise_std
enforce_parameter_constraints()
eval_over_all_parameters()
eval_over_all_settings()
get_setting()
good_setting()
last_setting_index
likelihood()
measurement_results
model_function
n_channels
opt_setting()
parameters
pdf_update()
pickiness
random_setting()
set_n_draws()
setting_indices
setting_values
utility()
utility_full_kld()
utility_max_min()
utility_pseudo()
utility_variance()
y_var_noise_model()
yvar_from_entropy()
yvar_from_parameter_draws()
yvar_max_min()
yvar_noise_model()
- OptBayesExptNoiseParam class
- OBE_Server class
- OBE_Socket class
- ParticlePDF class
- Utilities