Sökning: "probabilistic programming"
Visar resultat 1 - 5 av 25 avhandlingar innehållade orden probabilistic programming.
1. Probabilistic Programming for Birth-Death Models of Evolution
Sammanfattning : Phylogenetic birth-death models constitute a family of generative models of evolution. In these models an evolutionary process starts with a single species at a certain time in the past, and the speciations—splitting one species into two descendant species—and extinctions are modeled as events of non-homogenous Poisson processes. LÄS MER
2. A Probabilistic Model for Positional Voting - Spectrum Games -
Sammanfattning : This thesis considers a class of cooperative n-person games (voting games) in which the voters are spread across an ideological scale. The two most commonly used measures of individual voting power in voting games are the Shapley-Shubik index and the Banzhaf-Coleman index. In both indices, the actors are assumed anomymous and treatad symmetrically. LÄS MER
3. Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages
Sammanfattning : Probabilistic programming languages (PPLs) allow users to express statistical inference problems that the PPL implementation then, ideally, solves automatically. In particular, PPL users can focus on encoding their inference problems, and need not concern themselves with the intricacies of inference. LÄS MER
4. Probabilistic Models for Species Tree Inference and Orthology Analysis
Sammanfattning : A phylogenetic tree is used to model gene evolution and species evolution using molecular sequence data. For artifactual and biological reasons, a gene tree may differ from a species tree, a phenomenon known as gene tree-species tree incongruence. Assuming the presence of one or more evolutionary events, e.g. LÄS MER
5. Sequential Monte Carlo methods for conjugate state-space models
Sammanfattning : Bayesian inference in state-space models requires the solution of high-dimensional integrals, which is intractable in general. A viable alternative is to use sample-based methods, like sequential Monte Carlo, but this introduces variance into the inferred quantities that can sometimes render the estimates useless. LÄS MER