We investigate the transition processes between the emitting (ON) and non-emitting (OFF) states of fluorescent molecules using a machine-learning approach. In fluorescently labeled DNA, continuous ...

Although there are many measures of variability for qualitative variables, they are little used in social research, nor are they included in statistical software. The aim of this article is to present ...

Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery ...

There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, ...

LaDyBUGS is an efficient alchemical free energy method for computing free energy differences between two or more chemical states. LaDyBUGS uses Gibbs sampling to sample alchemical transformations ...

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In 2004, Skilling introduced the nested sampling algorithm 1,2 in the context of Bayesian inference and computation (Box 1). The nested sampling algorithm solves otherwise challenging, ...

Bayesian Networks are graphical models useful for various applications, including time series prediction and anomaly detection. Bayesian inference offers a robust set of tools for modelling ...

Genetic recombination processes, such as reassortment, make it complex or impossible to use standard phylogenetic and phylodynamic methods. This is due to the fact that the shared evolutionary history ...

Bayesian Statistics for Beginners. A Step-by-Step Approach (Donovan and Mickey, 2019) is, perhaps, the “truest-to-title” book I have read on Bayesian inference and statistics, insofar (a) it is ...

A central goal of computational physics and chemistry is to predict material properties by using first-principles methods based on the fundamental laws of quantum mechanics. However, the high ...