gns.nested_run module

gns.nested_run.NestedRun(priorFunc, invPriorFunc, LhoodFunc, paramNames, targetSupport, setupDict, LLhoodFunc=None, return_vals=False)

Wrapper around linear and log nested run functions.

Args:

priorFunc : function prior function

invPriorFunc : function inverse prior function

LhoodFunc : function likelihood function

paramNames : list parameter names

targetSupport : array target support values in array of shape (3, nDims)

setupDict : dict setup dictionary explained in README.md

LLhoodFunc : function log likelihood function

return_vals : bool whether to return statistics parameters of nested run or not, i.e. the expected log evidence E[ln(Z)], its variance var[ln(Z)] and the K-L divergence H. If False, writes these values to file instead (and prints to stdout).

gns.nested_run.NestedRunLinear(priorFunc, invPriorFunc, LhoodFunc, paramNames, targetSupport, setupDict, return_vals=False)

function which completes a NS run. parameters of priors and likelihood need to be specified, as well as a flag indication type of prior for each dimension and the pdf for the lhood. setupDict contains other setup parameters such as termination type & factor, method of finding new livepoint, details of how weights are calculated, how final Z contribution is added, and directory/file prefix for saved files.

Args:

priorFunc : function prior function

invPriorFunc : function inverse prior function

LhoodFunc : function likelihood function

paramNames : list parameter names

targetSupport : array target support values in array of shape (3, nDims)

setupDict : dict setup dictionary explained in README.md

return_vals : bool whether to return statistics parameters of nested run or not, i.e. the expected log evidence E[ln(Z)], its variance var[ln(Z)] and the K-L divergence H. If False, writes these values to file instead (and prints to stdout).

gns.nested_run.NestedRunLog(priorFunc, invPriorFunc, LLhoodFunc, paramNames, targetSupport, setupDict, return_vals=False)

function which completes a NS run. parameters of priors and likelihood need to be specified, as well as a flag indication type of prior for each dimension and the pdf for the lhood. setupDict contains other setup parameters such as termination type & factor, method of finding new livepoint, details of how weights are calculated, how final Z contribution is added, and directory/file prefix for saved files.

Args:

priorFunc : function prior function

invPriorFunc : function inverse prior function

LLhoodFunc : function log likelihood function

paramNames : list parameter names

targetSupport : array target support values in array of shape (3, nDims)

setupDict : dict setup dictionary explained in README.md

return_vals : bool whether to return statistics parameters of nested run or not, i.e. the expected log evidence E[ln(Z)], its variance var[ln(Z)] and the K-L divergence H. If False, writes these values to file instead (and prints to stdout).