Stochastic resetting in the Kramers problem: A Monte Carlo approach

Stochastic Methods: Monte Carlo Approach

Addressing the Correspondence Problem: A Markov Chain Monte Carlo Approach

Many vision and AI techniques assume that some form of the infamous correspondence problem has been solved. Typically, a best mapping between sets of features is found either as a pre-processing step or as a side-effect of applying the technique. In this paper we argue that it is incorrect to insist on a single ’best’ mapping between features in order to estimate a property that depends on this...

A Stochastic Monte Carlo Algorithm

We propose a Monte Carlo algorithm to promote Kennedy and Kuti’s linear accept/reject algorithm which accommodates unbiased stochastic estimates of the probability to an exact one. The probability upper bound violations are avoided by adopting the Metropolis accept/reject steps for both the dynamical and noise configurations and the lower bound violations can be absorbed into the observables. W...

A New Approach for Monte Carlo Simulation of RAFT Polymerization

In this work, based on experimental observations and exact theoretical predictions, the kinetic scheme of RAFT polymerization is extended to a wider range of reactions such as irreversible intermediate radical terminations and reversible transfer reactions. The reactions which have been labeled as kinetic scheme are the more probable existing reactions as the theoretical point of view. The ...

Stochastic Gradient Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a MetropolisHastings framework, enabling more efficient exploration of the state space than standard random-walk proposals. The popularity of such methods has grown significantly in recent years. However, a limitation of HMC methods is the required gradient com...