Sentence examples for multiple importance sampling from inspiring English sources

Cornuet et al. [20] improve this in their adaptive multiple importance sampling algorithm and present optimal exploitation of previous samples, which automatically stabilizes the process.

Our results were motivated by an inequality, involving harmonic means, found in the study of multiple importance sampling Monte Carlo technique.

We revisit the multiple importance sampling (MIS) estimator and investigate the bound on the efficiency improvement over balance heuristic estimator with equal count of samples established in Veach's thesis.

Motivated by proving an inequality that appeared in our research in Monte Carlo multiple importance sampling (MIS), we identified first a sufficient condition for a generalized weighted means inequality where only the weights are changed.

The multiple importance sampling (MIS) estimator [1, 2], and in particular balance heuristic, which is equivalent to the Monte Carlo estimator with a mixture of probability density functions (pdfs), has been used for many years with a big success, being a reliable and robust estimator that allows an easy and straightforward combination of different sampling techniques.

where F is any multiple importance sampling estimator using the same total number of samples N. Veach interpreted Theorem 9.5 as a proof of quasi-optimality of balance heuristic with equal count of samples, saying "According to this result, changing the N i can improve the variance by at most a factor of n, plus a small additive term.

In the research in the multiple importance sample Monte Carlo integration problem [1 3], we were confronted with several inequalities relating harmonic means, which were either described in the literature [4 7] or easy to prove from it.

The proof of Eq. 23 is based on the following inequality, which compares a general multiple importance sample estimator F with arbitrary number of samples {N i } with the same estimator (i.e., using the same weights w i ) but with equal count of samples, Feq as follows: begin{array}rcl@ V[!F] ge frac{1}{n} V[!F_{{text{eq}}}].

This importance sampling method applies multiple biases in the direction of photon scattering towards the apparent position of the OCT collecting fiber.

We show how to efficiently calculate the signal in optical coherence tomography (OCT) systems due to the ballistic photons, the quasi-ballistic photons, and the photons that undergo multiple diffusive scattering using Monte Carlo simulations with importance sampling.

Before describing the coupling of multiple chains, we introduce the general idea of importance sampling first: The approach is based on sampling from a different distribution, such that the region of interest is sampled with high probability.