Package trunk :: Package BIP :: Package Bayes :: Package Samplers :: Module MCMC :: Class Metropolis

Class Metropolis

object --+    
         |    
  _Sampler --+
             |
            Metropolis

Standard random-walk Metropolis Hastings sampler class

Instance Methods

[hide private]

__init__(self, meldobj, samples, sampmax, data, t, parpriors, parnames, parlimits, likfun, likvariance, burnin, **kwargs)
MCMC based fitting

source code

_propose(self, step, po=None)
Generates proposals. returns two lists

source code

step(self, nchains=1)
Does the actual sampling loop.

source code

_rms_fit(self, s1, s2)
Calculates a basic fitness calculation between a model- generated time series and a observed time series. It uses a normalized RMS variation.

source code

_imp_sample(self, n, data, w)
Importance sampling

source code

_add_salt(self, dataset, band)
Adds a few extra uniformly distributed data points beyond the dataset range. This is done by adding from a uniform dist.

source code

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, _tune_likvar, _watch_chain

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

Class Variables

[hide private]

Inherited from _Sampler: e, liklist, seqhist, trace_acceptance, trace_convergence

Inherited from _Sampler (private): _R, _dimensions, _j, _po

Properties

[hide private]

Inherited from object: __class__

Method Details

[hide private]

init(self, meldobj, samples, sampmax, data, t, parpriors, parnames, parlimits, likfun, likvariance, burnin, **kwargs)
(Constructor)

source code

MCMC based fitting

Parameters:

samples - : Number of samples to obtain
sampmax - : Maximum number of samples drawn.
data - : observed time series on the model's output
t - : length of the observed time series
parpriors - : Dictionary with frozen distributions objects as values and parnames as keys
parnames - : List of parameter names
parlimits - : list of tuples with (min,max) for every parameter.
likfun - : Likelihood function
likvariance - : variance of the Normal likelihood function
burnin - : Number of burnin samples

Overrides: object.__init__

_propose(self, step, po=None)

source code

Generates proposals. returns two lists

Parameters:

step - : Position in the markov chain history.
po - : Process pool for parallel proposal generation

Returns:

theta: List of proposed self.dimensional points in parameter space
prop: List of self.nchains proposed phis.

Overrides: _Sampler._propose

_rms_fit(self, s1, s2)

source code

Calculates a basic fitness calculation between a model- generated time series and a observed time series. It uses a normalized RMS variation.

s1 and s2 can also be both lists of lists or lists of arrays of the same length.

Parameters:

s1 (: Record array or list.) - : model-generated time series.
s2 (: Dictionary or list) - : observed time series. dictionary with keys matching names of s1

Returns:

Inverse of the Root mean square deviation between s1 and s2.

_imp_sample(self, n, data, w)

source code

Importance sampling

Parameters:

n - : Number of samples to return
data - : record array (containing on or more vectors of data) to be resampled
w - : Weight vector

Returns:

returns a sample of size n

_add_salt(self, dataset, band)

source code

Adds a few extra uniformly distributed data points beyond the dataset range. This is done by adding from a uniform dist.

Parameters:

dataset - : vector of data
band - : Fraction of range to extend: [0,1[

Returns:

Salted dataset.

Class Metropolis

__init__(self, meldobj, samples, sampmax, data, t, parpriors, parnames, parlimits, likfun, likvariance, burnin, **kwargs) (Constructor)

_propose(self, step, po=None)

_rms_fit(self, s1, s2)

_imp_sample(self, n, data, w)

_add_salt(self, dataset, band)

init(self, meldobj, samples, sampmax, data, t, parpriors, parnames, parlimits, likfun, likvariance, burnin, **kwargs)
(Constructor)