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Bootstrap MarkovChain MonteCarlo algorithm. This can be used to generate samples from a probability distribution, or also as a simulated annealing algorithm for maximization. This can be imported and its functions and classes can be used.
The central interfaces are the BootStepper class, and within it, the BootStepper.step method is used iteratively to take a Markov step. To use this module, the user should derive a class from the problem_definition class and redefine (at least) the problem_definition.logp method.
It was originally inspired by amoeba_anneal (in Numerical Recipes, Press et al.). The essential feature is that it keeps a large archive of previous positions (possibly many times more than N of them). It samples two positions from the archive and subtracts them to generate candidate steps. This has the nice property that when sampling from a multivariate Gaussian distribution, the candidate steps match the distribution nicely.
It can be operated in two modes (or set to automatically switch). One is optimization mode where it heads for the maximum of the probability distribution. The other mode is sampling mode, where it asymptotically follows a Markov sampling procedure and has the proper statistical properties.
The algorithm has been described in:
An earlier version has been used in:
Classes  
problem_definition This class implements the problem to be solved. 

problem_definition_F This is a variant that is used bin the bin/mcmc.py script. 

position_base This class is used internally in the MCMC sampling process to represent a position. 

position_repeatable This is for the common case where logp is a wellbehaved function of its arguments. 

position_nonrepeatable This is for the (unfortunately common) case where logp is an indpendent random function of its arguments. 

acceptor_base  
T_acceptor This class implements a normal MetropolisHastings acceptance of steps. 

rough_acceptor_base  
rough_T_acceptor This class implements a normal MetropolisHastings acceptance of steps. 

stepper This is your basic stepper class. 

adjuster The adjuster class controls the step size.


NoBoot This is used internally to signify that some particular method of selecting a step has decided that it is not suitable. 

NotGoodPosition This is raised by user code (e.g. 

hashcounter_c Keep a count of how many times we've see various items. 

Archive This maintains a list of all the recent accepted positions. 

ContPrmArchive  
BootStepper The BootStepper class is the primary interface. 
Functions  













Variables  
Debug = 0


MEMORYS_WORTH_OF_PARAMETERS = 100000000.0 How many bytes can we use to store the archives? 

__package__ =

Imports: math, types, random, bisect, threading, numpy, die, gpkmisc, g_implements, MVN
Function Details 
Turn almost anything into a list of position_base objects. You can hand it a sequence of numpy vectors or a single 1dimensional numpy vector; a sequence of position_base objects or a single 1dimensional position_base object. Precondition: This depends on PositionClass being callable as PositionClass(vector_of_doubles, problem_definition). 
This is (essentially) another interface to the class constructor. It's really there for backwards compatibility. 
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