Summary This article introduces the hug and hop Markov chain Monte Carlo algorithm for estimating expectations with respect to an intractable distribution. The alternates between two kernels, referred as hop. Hug is a nonreversible kernel that repeatedly applies bounce mechanism from recently proposed bouncy particle sampler produce proposal point far current position yet on almost same contour...

Approximating the stationary probability of a state in a Markov chain through Markov chain Monte Carlo techniques is, in general, inefficient. Standard random walk approaches require Õ(τ/π(v)) operations to approximate the probability π(v) of a state v in a chain with mixing time τ , and even the best available techniques still have complexity Õ(τ/π(v)); and since these complexities depend inve...

Many currently used algorithms for protein coding sequences require large learning sets of true genes to estimate sensible values for used parameters which are necessary to make the prediction reasonable. They also fail in recognition of short genes which usually contain weak coding signal. To avoid these problems, we worked out a new algorithm for finding protein coding potential in prokaryoti...