Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...
The rapid accumulation of genome sequence data has made phylogenetics an indispensable tool to various branches of biology. However, it has also posed considerable statistical and computational ...
In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...
If the reported data of an experiment have been subject to selection, then inference from such data should be modified accordingly. We investigate the modification required to the face-value ...
The Engine for Likelihood-Free Inference is open to everyone, and it can help significantly reduce the number of simulator runs. The Engine for Likelihood-Free Inference is open to everyone, and it ...
• Background and Aims For 84 years, botanists have relied on calculating the highest common factor for series of haploid chromosome numbers to arrive at a so-called basic number, x. This was done ...
Background Bayesian networks (BN) are directed acyclic graphs derived from empirical data that describe the dependency and probability structure. It may facilitate understanding of complex ...
The Engine for Likelihood-Free Inference is open to everyone, and it can help significantly reduce the number of simulator runs. Researchers have succeeded in building an engine for likelihood-free ...
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