Independent¶
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class
pyhf.probability.
Independent
(batched_pdf, batch_size=None)[source]¶ Bases:
pyhf.probability._SimpleDistributionMixin
A probability density corresponding to the joint distribution of a batch of identically distributed random variables.
Example
>>> import pyhf >>> import numpy.random as random >>> random.seed(0) >>> rates = pyhf.tensorlib.astensor([10.0, 10.0]) >>> poissons = pyhf.probability.Poisson(rates) >>> independent = pyhf.probability.Independent(poissons) >>> independent.sample() array([10, 11])
Methods
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__init__
(batched_pdf, batch_size=None)[source]¶ - Parameters
batched_pdf (pyhf.probability distribution) – The batch of pdfs of the same type (e.g. Poisson)
batch_size (int) – The size of the batch
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log_prob
(value)[source]¶ The log of the probability density function at the given value. As the distribution is a joint distribution of the same type, this is the sum of the log probabilities of each of the distributions the compose the joint.
Example
>>> import pyhf >>> import numpy.random as random >>> random.seed(0) >>> rates = pyhf.tensorlib.astensor([10.0, 10.0]) >>> poissons = pyhf.probability.Poisson(rates) >>> independent = pyhf.probability.Independent(poissons) >>> values = pyhf.tensorlib.astensor([8.0, 9.0]) >>> independent.log_prob(values) -4.262483801927939 >>> broadcast_value = pyhf.tensorlib.astensor([11.0]) >>> independent.log_prob(broadcast_value) -4.347743645878765
- Parameters
value (tensor or float) – The value at which to evaluate the distribution
- Returns
The value of \(\log(f\left(x\middle|\theta\right))\) for \(x=\)
value
- Return type
Tensor
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