trunk.BIP.Bayes.Samplers.MCMC.Dream

Class Dream

object --+ | _Sampler --+ | Dream

Instance Methods

__init__(self, meldobj, samples, sampmax, data, t, parpriors, parnames, parlimits, likfun, likvariance, burnin, thin=5, convergenceCriteria=1.1, nCR=3, DEpairs=1, adaptationRate=.65, eps=5e-6, mConvergence=False, mAccept=False, **kwargs)
x.__init__(...) initializes x; see x.__class__.__doc__ for signature

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_det_outlier_chains(self, step)
Determine which chains are outliers

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delayed_rejection(self, xi, zi, pxi, zprob)
Generates a second proposal based on rejected proposal xi

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_alpha1(self, p1, p2)
Returns the Metropolis acceptance probability: alpha1(p1,p1) = min(1,p1/p2) if p2 >-np.inf else 1

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update_CR_dist(self)

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_prop_initial_theta(self, step)
Generate Theta proposals from priors

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_prop_phi(self, thetalist, po=None)
Returns proposed Phi derived from theta

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_chain_evolution(self, proptheta, propphi, pps, liks)
Chain evolution as describe in ter Braak's Dream algorithm.

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_get_post_prob(self, theta, prop, po=None)
Calculates the posterior probability for the proposal of each chain

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step(self)
Does the actual sampling loop.

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Inherited from _Sampler: DIC, best_prop_index, check_constraints, dimensions, gr_R, gr_convergence, po, setup_xmlrpc_plotserver, shut_down, shutdown_xmlrpc_plotserver, term_pool

Inherited from _Sampler (private): _accept, _every_plot, _propose, _tune_likvar, _watch_chain

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties

[hide private]

Inherited from object: __class__

init(self, meldobj, samples, sampmax, data, t, parpriors, parnames, parlimits, likfun, likvariance, burnin, thin=5, convergenceCriteria=1.1, nCR=3, DEpairs=1, adaptationRate=.65, eps=5e-6, mConvergence=False, mAccept=False, **kwargs)
(Constructor)

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x.__init__(...) initializes x; see x.__class__.__doc__ for signature

Overrides: object.__init__: (inherited documentation)

_alpha1(self, p1, p2)

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Returns the Metropolis acceptance probability: alpha1(p1,p1) = min(1,p1/p2) if p2 >-np.inf else 1

Parameters:

p1 - : log probability
p2 - : log probability

Decorators:

@np.vectorize

_get_post_prob(self, theta, prop, po=None)

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Calculates the posterior probability for the proposal of each chain

Parameters:

theta - : list of nchains thetas
prop - : list of nchains phis
po - : Pool of processes

Returns:

posts: list of log posterior probabilities of length self.nchains
listoliks: list of log-likelihoods of length self.nchains

Class Dream

__init__(self, meldobj, samples, sampmax, data, t, parpriors, parnames, parlimits, likfun, likvariance, burnin, thin=5, convergenceCriteria=1.1, nCR=3, DEpairs=1, adaptationRate=.65, eps=5e-6, mConvergence=False, mAccept=False, **kwargs) (Constructor)

_alpha1(self, p1, p2)

_get_post_prob(self, theta, prop, po=None)

init(self, meldobj, samples, sampmax, data, t, parpriors, parnames, parlimits, likfun, likvariance, burnin, thin=5, convergenceCriteria=1.1, nCR=3, DEpairs=1, adaptationRate=.65, eps=5e-6, mConvergence=False, mAccept=False, **kwargs)
(Constructor)