Annealed importance sampling is a means to assign equilibrium weights to a nonequilibrium sample that was generated by a simulated annealing protocol[1]. The weights may then be used to calculate equilibrium averages, and also serve as an “adiabatic signature” of the chosen cooling schedule. In this paper we demonstrate the method on the 50-atom dileucine peptide, showing that equilibrium distr...

The problem of noise in Monte-Carlo rendering arising from estimator variance is well-known and well-studied. In this work, we concentrate on identifying individual light paths as outliers that lead to significant spikes of noise and represent a challenge for existing filtering methods. Most noise-reduction methods, such as importance sampling and stratification, attempt to generate samples tha...

Importance sampling is a widely used variance reduction technique to compute sample quantiles such as value at risk. The variance of the weighted sample quantile estimator is usually a difficult quantity to compute. In this paper we present the exact convergence rate and asymptotic distributions of the bootstrap variance estimators for quantiles ofweighted empirical distributions. Under regular...

We propose simulation based estimation for discrete sequential move games of perfect information which relies on the simulated moments and importance sampling. We use importance sampling techniques not only to reduce computational burden and simulation error, but also to overcome non-smoothness problems. The model is identified with only weak scale and location normalizations, monte Carlo evide...

An importance sampling (IS) simulation method is presented for evaluating the lower-bound symbol error rate (SER) of the Bayesian decision feedback equalizer (DFE) with M -PAM symbols, under the assumption of correct decision feedback. By exploiting an asymptotic property of the Bayesian DFE, a design procedure is developed, which chooses appropriate bias vectors for the simulation density to e...

An importance sampling (IS) simulation method is presented for evaluating the lower-bound symbol error rate (SER) of the Bayesian decision feedback equalizer (DFE) with -PAM symbols, under the assumption of correct decision feedback. By exploiting an asymptotic property of the Bayesian DFE, a design procedure is developed, which chooses appropriate bias vectors for the simulation density to ens...

In a previous paper we introduced a new variance-reduction technique for regenerative simulations based on permuting regeneration cycles. In this paper we apply this idea to new classes of estimators. In particular, we derive permuted versions of likelihood-ratio derivative estimators for steady-state performance measures, importance-sampling estimators of the mean cumulative reward until hitti...

Surles and Padgett [15] introduced two-parameter Burr Type X distribution, which can be described as a generalized Rayleigh distribution. In this paper we consider the estimation of the stress-strength parameter R = P [Y < X], when X and Y are both three-parameter generalized Rayleigh distribution with the same scale and locations parameters but different shape parameters. It is assumed that th...

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We show that the Gibbs sampler can be combined with a unidimensional deterministic integration rule applied...

We present a new estimator for counting the number of solutions of a Boolean satisfiability problem as a part of an importance sampling framework. The estimator uses the recently introduced SampleSearch scheme that is designed to overcome the rejection problem associated with distributions having a substantial amount of determinism. We show here that the sampling distribution of SampleSearch ca...