Abstract The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to
modeling disease progression due to its ability to describe noisy observations arriving …

Efficient algorithms and programs for the analysis of the ever-growing amount of biological
sequence data are strongly needed in the genomics era. The pace at which new data and …

The measurement of synonymous and nonsynonymous substitution rates (d S and d N) is
useful for assessing selection operating on protein sequences or for investigating mutational …

Genes and genomes do not evolve similarly in all branches of the tree of life. Detecting and
characterizing the heterogeneity in time, and between lineages, of the nucleotide (or amino …

Sequential data naturally arises from user engagement on digital platforms like social
media, music streaming services, and web navigation, encapsulating evolving user …

Offering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists,
systematists, ecologists, and epidemiologists to obtain answers to very detailed …

The analysis of extant sequences shows that molecular evolution has been heterogeneous
through time and among lineages. However, for a given sequence alignment, it is often …

Inference of gene sequences in ancestral species has been widely used to test hypotheses
concerning the process of molecular sequence evolution. However, the approach may …

Bayesian Analysis of Continuous Time Markov Chains with Application to Phylogenetic
Modelling Page 1 Bayesian Analysis (2016) 11, Number 4, pp. 1203–1237 Bayesian Analysis …

Markov models are a simple and powerful tool for analyzing the health and economic effects
of health care interventions. These models are usually evaluated in discrete time using …