The use of fossil evidence to calibrate divergence time estimation has a long history. More recently, Bayesian Markov chain Monte Carlo has become the dominant method of divergence time estimation, and fossil evidence has been reinterpreted as the specification of prior distributions on the divergence times of calibration nodes. These so-called "soft calibrations" have become widely used but th...

Aimed at the divergence problem in the traditional Kalman Filtering (KF) channel estimation algorithm due to the inaccurate state transfer coefficient (STC), this paper proposes a novel KF channel estimation method using the STC with threshold correction. By setting a reasonable threshold, the estimation performance of STC can be greatly improved in the time-varying Ultra-Wideband (UWB) channel...

Understanding the processes and conditions under which populations diverge to give rise to distinct species is a central question in evolutionary biology. Since recently diverged populations have high levels of shared polymorphisms, it is challenging to distinguish between recent divergence with no (or very low) inter-population gene flow and older splitting events with subsequent gene flow. Re...

We present an estimation of divergence time-range based on a coalescent model. This model sum the probability of coalescent trees, taking into account the effect of incomplete lineage sorting. Maximum likelihood estimate based on this model has been computed previously; however, a formula for divergence time-range estimate or confidence interval has never been presented for this model as the ex...

UNLABELLED Ever larger phylogenies are being constructed due to the explosion of genetic data and development of high-performance phylogenetic reconstruction algorithms. However, most methods for calculating divergence times are limited to datasets that are orders of magnitude smaller than recently published large phylogenies. Here, we present an algorithm and implementation of a divergence tim...

Bayesian methods for estimating evolutionary divergence times are extended to multigene data sets, and a technique is described for detecting correlated changes in evolutionary rates among genes. Simulations are employed to explore the effect of multigene data on divergence time estimation, and the methodology is illustrated with a previously published data set representing diverse plant taxa. ...

A fundamental problem with the modeling of chaotic time series data is that minimizing short-term prediction errors does not guarantee a match between the reconstructed attractors of model and experiments. We introduce a modeling paradigm that simultaneously learns to short-term predict and to locate the outlines of the attractor by a new way of nonlinear principal component analysis. Closed-lo...

Estimates of molecular divergence times when rates of evolution vary require the assumption of a model of rate change. Brownian motion is one such model, and since rates cannot become negative, a log Brownian model seems appropriate. Divergence time estimates can then be made using weighted least squares penalties. As sequences become long, this approach effectively becomes equivalent to penali...