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The BootStepper class is the primary interface. It implements a Bootstrap Markov Chain Monte Carlo stepper. The class instance stores the necessary state information, and each call to step() takes another step.
Instance Methods  




















position_base 




numpy.ndarray





int 


Inherited from 
Class Variables  
F = 0.234 F is the targeted step acceptance rate. 

alpha = 0.1 How rapidly should one expand the archive after a reset? 

PBootLim = 0.9 PBootLim Limits the probability of taking a bootstrap step. 

SSAMPLE =
Sampling mode 

SUPHILL =
Go straight uphill 

SANNEAL =
Simulated annealing 

SSAUTO =


SSNEVER =


SSLOW =


SSALWAYS =

Instance Variables  
np The number of parameters: 

np_eff In a multiprocessor situation, np_eff tells you how much data do you need to store locally, so that the overall group of processors stores enough variety of data. 

acceptable Acceptable is a function that decides whether or not a step is OK. (Inherited from gmisclib.mcmc.stepper) 

last_failed It should reflect the success or failure of the most recently completed step. (Inherited from gmisclib.mcmc.stepper) 
Properties  
Inherited from 
Method Details 
x.__init__(...) initializes x; see help(type(x)) for signature

In subclasses, this takes a step and returns 0 or 1, depending on whether the step was accepted or not.

Provides some printable status information in a=v; format.

A crude measure of how ergodic the MCMC is.

Class Variable Details 
FF is the targeted step acceptance rate. This is from G. O. Roberts, A. Gelman, and W. Gilks (1997) "Weak convergence and optimal scaling of random walk Metropolis algorithm." Ann. Applied. Probability, 7, p. 110120 and also from G. O. Roberts and J. S. Rosenthal (2001) "Optimal scaling of various MetropolisHastings algorithms." Statistical Sci. 16, pp. 351367.

PBootLimPBootLim Limits the probability of taking a bootstrap step. This, if the optimization collapses into a subspace, some other kind of step will eventually get it out.

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