Exact ground state Monte Carlo method for Bosons without importance sampling

Modified Monte Carlo with Importance Sampling Method

Monte Carlo simulation methods apply a random sampling and modifications can be made of this method is by using variance reduction techniques (VRT). VRT objective is to reduce the variance due to Monte Carlo methods become more accurate with a variance approaching zero and the number of samples approaches infinity, which is not practical in the real situation (Chen, 2004). These techniques are ...

Quantum algorithm for exact Monte Carlo sampling.

We build a quantum algorithm which uses the Grover quantum search procedure in order to sample the exact equilibrium distribution of a wide range of classical statistical mechanics systems. The algorithm is based on recently developed exact Monte Carlo sampling methods, and yields a polynomial gain compared to classical procedures.

Greedy importance sampling: A new Monte Carlo inference method

This paper presents a new Monte Carlo inference method that is a simple variation of importance sampling. It is well known that importance sampling fails when the proposal distribution concentrates samples in low density regions of the target distribution, which leads to high variance in the estimates. The method we propose attempts to mitigate this difficulty by explicitly searching for high d...

Monte Carlo inference via greedy importance sampling

We present a new method for conducting Monte Carlo inference in graphical models which combines explicit search with generalized importance sampling. The idea is to reduce the variance of importance sampling by searching for significant points in the target distribution. We prove that it is possible to introduce search and still maintain unbiasedness. We then demonstrate our procedure on a few ...

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities