Package csb :: Package statistics :: Module scalemixture :: Class ScaleMixture |
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# Class ScaleMixture

source code

             object --+
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    pdf.AbstractDensity --+
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            pdf.BaseDensity --+
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                             ScaleMixture

Robust probabilistic superposition and comparison of protein structures Martin Mechelke and Michael Habeck

Represenation of a distribution as a mixture of gaussians with a mean of zero and different inverse variances/scales. The number of scales equals the number of datapoints.

The underlying family is determined by a prior ScaleMixturePrior on the scales. Choosing a GammaPrior results in Stundent-t posterior, wheras a InvGammaPrior leads to a K-Distribution as posterior.

Nested Classes
Inherited from `pdf.AbstractDensity`: `__metaclass__`

Instance Methods
__init__(self, scales=array([ 1., 1.]), prior=None, d=3) x.__init__(...) initializes x; see help(type(x)) for signature | source code
array |
log_prob(self, x) Evaluate the logarithm of the probability of observing values `x`. | source code
random(self, shape=None) Generate random samples from the probability distribution. | source code
Inherited from `pdf.AbstractDensity`: `__call__`, `__getitem__`, `__setitem__`, `__str__`, `estimate`, `evaluate`, `get_params`, `set_params` Inherited from `object`: `__delattr__`, `__format__`, `__getattribute__`, `__hash__`, `__new__`, `__reduce__`, `__reduce_ex__`, `__repr__`, `__setattr__`, `__sizeof__`, `__subclasshook__`

Class Variables
| __abstractmethods__ = ``frozenset([``])``

Properties
| prior
| scales
Inherited from `pdf.AbstractDensity`: `estimator`, `parameters` Inherited from `object`: `__class__`

Method Details

### __init__(self, scales=array([ 1., 1.]), prior=None, d=3) (Constructor) | source code
x.__init__(...) initializes x; see help(type(x)) for signature Overrides: object.__init__ (inherited documentation)

### log_prob(self, x) | source code
Evaluate the logarithm of the probability of observing values `x`. Parameters: `x` - values Returns: array Overrides: pdf.AbstractDensity.log_prob (inherited documentation)

### random(self, shape=None) | source code
Generate random samples from the probability distribution. Parameters: `size` - number of values to sample Overrides: pdf.AbstractDensity.random (inherited documentation)

Property Details

### prior Get Method: unreachable.prior(self) Set Method: unreachable.prior(self, value)

### scales Get Method: unreachable.scales(self) Set Method: unreachable.scales(self, value)