Bayesian evolutionary analysis provide a statistically sound and flexible framework for estimation of evolutionary parameters. In this method, posterior estimates of evolutionary rate (μ) are derived by combining evolutionary information in the data with researcher’s prior knowledge about the true value of μ. Nucleotide sequence samples of fast evolving pathogens that are taken at d...

Uncertainty in divergence time estimation is frequently studied from many angles but rarely from the perspective of phylogenetic node age. If appropriate molecular models and fossil priors are used, a multi-locus, partitioned analysis is expected to equally minimize error in accuracy and precision across all nodes of a given phylogeny. In contrast, if available models fail to completely account...

Time-scaled phylogenetic trees are essential tools in modern biology and node-based calibrations have been the main approach to time-tree estimation. But methods for generating required calibration information scarce difficult parameterize. Here, I present CladeDate, an R package generation of empirical from fossil record. CladeDate uses simple mathematical models estimate age a clade its uncer...

The subtribe Eritrichiinae belongs to tribe Rochelieae (Borginaceae; Cynoglossoideae) which is composed of about 200 species in five genera including Eritrichium, Lappula, Hackelia, Lepechiniella, and Rochelia. The majority of the species are annual and grow in xeric habitats. The genus Lappula as an arid adapted and the second biggest genus...

Abstract The main purpose of the presented paper is to obtain some time scale inequalities for different divergences and distances by using weighted scales Jensen’s inequality. These results offer new in h -discrete calculus quantum extend known literature. lower bounds divergence measures are also presented. Moreover, obtained discrete given light Zipf–Mandelbrot law Zipf law.

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the relative Pearson divergence as a divergence measure, and it is accurately and eff...