Birth-and-Death Processes in Python: The BirDePy Package

Birth-and-Death Processes in Python: The BirDePy Package

ABC algorithm, birth-and-death processes, continuous-time Markov chains, diffusion approximation, EM algorithm, Erlangization, Laplace transform, maximum likelihood
estimation, parameter estimation, Python, uniformization.

Birth-and-death processes (BDPs) form a class of continuous-time Markov chains that
are particularly suited to describing the changes in the size of a population over time.
Population-size-dependent BDPs (PSDBDPs) allow the rate at which a population grows
to depend on the current population size. The main purpose of our new Python package BirDePy is to provide easy-to-use functions that allow the parameters of discretelyobserved PSDBDPs to be estimated. The package can also be used to estimate parameters
of continuously-observed PSDBDPs, simulate sample paths, approximate transition probabilities, and generate forecasts. We describe in detail several methods which have been
incorporated into BirDePy to achieve each of these tasks. The usage and effectiveness of
the package is demonstrated through a variety of examples of PSDBDPs, as well as case
studies involving annual population count data of two endangered bird species.

Sophie Hautphenne

https://www.jstatsoft.org/article/view/v111i05

The University of Melbourne

November 2024

Fajar bagus W

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English

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