Prior

General prior object

class pocomc.prior.Prior(dists=None)

A class for priors.

Parameters:

dists (list of scipy.stats distributions) – A list of distributions for each parameter. The length of the list determines the dimension of the prior.

dists

A list of distributions for each parameter. The length of the list determines the dimension of the prior.

Type:

list of scipy.stats distributions

bounds

An array of shape (dim, 2) containing the lower and upper bounds for each parameter.

Type:

ndarray

dim

The dimension of the prior.

Type:

int

logpdf(x)

Returns the log of the probability density function evaluated at x.

rvs(size=1)

Returns a random sample from the prior.

Examples

>>> import numpy as np
>>> from scipy.stats import norm, uniform
>>> from pocomc.prior import Prior
>>> dists = [norm(loc=0, scale=1), uniform(loc=0, scale=1)]
>>> prior = Prior(dists)
>>> prior.logpdf(np.array([0, 0]))
-1.8378770664093453
>>> prior.rvs()
array([0.417022  , 0.72032449])
>>> prior.bounds
array([[-inf,  inf],
       [ 0. ,  1. ]])
>>> prior.dim
2

Notes

The logpdf method is implemented as a sum of the logpdf methods of the individual distributions. This is equivalent to assuming that the parameters are independent.

The rvs method is implemented by sampling from each distribution independently and then transposing the result. This is equivalent to assuming that the parameters are independent.

The bounds property is implemented by calling the support method of each distribution. This is equivalent to assuming that the parameters are independent.

The dim property is implemented by returning the length of the dists attribute. This is equivalent to assuming that the parameters are independent.

property bounds

An array of shape (dim, 2) containing the lower and upper bounds for each parameter.

Examples

>>> import numpy as np
>>> from scipy.stats import norm, uniform
>>> from pocomc.prior import Prior
>>> dists = [norm(loc=0, scale=1), uniform(loc=0, scale=1)]
>>> prior = Prior(dists)
>>> prior.bounds
array([[-inf,  inf],
       [ 0. ,  1. ]])

property dim

The dimension of the prior.

Examples

>>> import numpy as np
>>> from scipy.stats import norm, uniform
>>> from pocomc.prior import Prior
>>> dists = [norm(loc=0, scale=1), uniform(loc=0, scale=1)]
>>> prior = Prior(dists)
>>> prior.dim
2

logpdf(x)

Returns the log of the probability density function evaluated at x.

Parameters:

x (ndarray) – An array of shape (n, dim) containing n samples of the parameters.

Returns:

logp – An array of shape (n,) containing the log of the probability density function evaluated at each sample.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from scipy.stats import norm, uniform
>>> from pocomc.prior import Prior
>>> dists = [norm(loc=0, scale=1), uniform(loc=0, scale=1)]
>>> prior = Prior(dists)
>>> prior.logpdf(np.array([0, 0]))
-1.8378770664093453
>>> prior.logpdf(np.array([[0, 0], [0, 0]]))
array([-1.83787707, -1.83787707])

rvs(size=1)

Returns a random sample from the prior.

Parameters:

size (int, optional) – The number of samples to return. The default is 1.

Returns:

samples – An array of shape (size, dim) containing the samples.

Return type:

ndarray

Examples

>>> import numpy as np
>>> from scipy.stats import norm, uniform
>>> from pocomc.prior import Prior
>>> dists = [norm(loc=0, scale=1), uniform(loc=0, scale=1)]
>>> prior = Prior(dists)
>>> prior.rvs()
array([0.417022  , 0.72032449])
>>> prior.rvs(size=2)
array([[0.417022  , 0.72032449],
       [0.00011438, 0.30233257]])