Priors¶

class
orbitize.priors.
GaussianPrior
(mu, sigma, no_negatives=True)[source]¶ Gaussian prior.
\[log(p(x\sigma, \mu)) \propto \frac{(x  \mu)}{\sigma}\] Parameters
mu (float) – mean of the distribution
sigma (float) – standard deviation of the distribution
no_negatives (bool) – if True, only positive values will be drawn from this prior, and the probability of negative values will be 0 (default:True).
(written) Sarah Blunt, 2018

compute_lnprob
(element_array)[source]¶ Compute log(probability) of an array of numbers wrt a Gaussian distibution. Negative numbers return a probability of inf.
 Parameters
element_array (float or np.array of float) – array of numbers. We want the probability of drawing each of these from the appopriate Gaussian distribution
 Returns
array of log(probability) values, corresponding to the probability of drawing each of the numbers in the input element_array.
 Return type
numpy array of float

draw_samples
(num_samples)[source]¶ Draw positive samples from a Gaussian distribution. Negative samples will not be returned.
 Parameters
num_samples (float) – the number of samples to generate
 Returns
samples drawn from the appropriate Gaussian distribution. Array has length num_samples.
 Return type
numpy array of float

class
orbitize.priors.
LinearPrior
(m, b)[source]¶ Draw samples from the probability distribution:
\[p(x) \propto mx+b\]where m is negative, b is positive, and the range is [0,b/m].
 Parameters
m (float) – slope of line. Must be negative.
b (float) – y intercept of line. Must be positive.

class
orbitize.priors.
LogUniformPrior
(minval, maxval)[source]¶ This is the probability distribution \(p(x) \propto 1/x\)
The __init__ method should take in a “min” and “max” value of the distribution, which correspond to the domain of the prior. (If this is not implemented, the prior has a singularity at 0 and infinite integrated probability).
 Parameters
minval (float) – the lower bound of this distribution
maxval (float) – the upper bound of this distribution

class
orbitize.priors.
Prior
[source]¶ Abstract base class for prior objects. All prior objects should inherit from this class.
Written: Sarah Blunt, 2018

class
orbitize.priors.
SinPrior
[source]¶ This is the probability distribution \(p(x) \propto sin(x)\)
The domain of this prior is [0,pi].

class
orbitize.priors.
UniformPrior
(minval, maxval)[source]¶ This is the probability distribution p(x) propto constant.
 Parameters
minval (float) – the lower bound of the uniform prior
maxval (float) – the upper bound of the uniform prior

orbitize.priors.
all_lnpriors
(params, priors)[source]¶ Calculates log(prior probability) of a set of parameters and a list of priors
 Parameters
params (np.array) – size of N parameters
priors (list) – list of N prior objects corresponding to each parameter
 Returns
prior probability of this set of parameters
 Return type
float