ExponentialPrior

class ExponentialPrior

Enacts a prior expectation that the generation of the most recent common ancestor (MRCA) between extant hereditary stratigraphic columns becomes exponentialy less likely with increasing antiquity.

This prior provides a continuous approximation of the geometric distribution of time to MRCA expected under the Wright-Fisher model [1].

Populations. In Mathematical Statistical Physics (pp. 489-545). Elsevier. https://doi.org/10.1016/S0924-8099(06)80048-X

Notes

A static factory function to init an instance with a growth factor calculated from contextual information like population size and the number of generations elapsed since genesis should be made available in the future.

See Also

GeometricPrior: An exact, discrete analog of this prior.

__init__(growth_factor: float)[source]

Methods

CalcIntervalConditionedMean(begin_rank: int, end_rank: int) → float[source]

Calculate the centriod of prior probability mass within an interval of possible MRCA generations.

Parameters

begin_rankint: The starting rank of the interval, inclusive.
end_rankint: The ending rank of the interval, exclusive.

Returns

float: The prior expected generation of MRCA conditioned on the assumption that the MRCA falls within the given interval.

CalcIntervalProbabilityProxy(begin_rank: int, end_rank: int) → float[source]

Characterize the prior probability of the MRCA generation falling within an interval range.

Parameters

begin_rankint: The starting rank of the interval, inclusive.
end_rankint: The ending rank of the interval, exclusive.

Returns

float: The proxy statistic, proportional to the true estimated interval probability of the MRCA value by a fixed (but unspecified) constant proportion.

SampleIntervalConditionedValue(begin_rank: int, end_rank: int) → int[source]

Sample a generation of the MRCA conditioned on the assumption that the MRCA falls within the given interval.

Parameters

begin_rankint: The starting rank of the interval, inclusive.
end_rankint: The ending rank of the interval, exclusive.

Returns

int: A sampled generation of the MRCA, conditioned on the assumption that the MRCA falls within the given interval.

__init__(growth_factor: float)[source]