Recently, Heitz and D’Eon [2014] proposed a method for importance sampling the distribution of visible normals in the context of microfacet BSDF models. One of their sampling routines internally relies on a discontinuous mapping, which can cause problems in conjunction with Quasi Monte Carlo sampling and Markov Chain Monte Carlo integration. In this report, we develop an alternative method that...

A method employing decomposition techniques and Monte Carlo sampling (importance sampling) to solve stochastic linear programs is described and applied to capacity expansion planning problems of electric utilities. We consider uncertain availability of generators and transmission lines and uncertain demand. Numerical results are presented.

Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed-form expressions for the implied data density are not available. This is a common and well-known problem for most parameter driven dynamic econometric models. This paper reviews, adapts and compares three different approaches for solving this problem. For evaluating the likelihood...

Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggr...

We deene new selection criteria for lattice rules for quasi-Monte Carlo integration. The criteria examine the projections of the lattice over subspaces of small or successive dimensions. Their computation exploits the dimension-stationarity of certain lattice rules, and of other low-discrepancy point sets sharing this property. Numerical results illustrate the usefulness of these new gures of m...

A new scheme for detector generation for the Real-Valued Negative Selection Algorithm (RNSA) is presented. The proposed method makes use of genetic algorithms and Quasi-Monte Carlo Integration to automatically generate a small number of very efficient detectors. Results have demonstrated that a fault detection system with detectors generated by the proposed scheme is able to detect faults in an...

A robust and efficient object tracking algorithm is required in a variety of computer vision applications. Although various modern trackers have impressive performance, some challenges such as occlusion and target scale variation are still intractable, especially in the complex scenarios. This paper proposes a robust scale adaptive tracking algorithm to predict target scale by a sequential Mont...

A computational technique that transform integrals over R , or some of its subsets, into the hypercube [0, 1] can be exploited in order to solve integrals via Monte Carlo integration without the need to simulate from the original distribution; all that is needed is to simulate iid uniform [0, 1] pseudo random variables. In particular the technique arises from the copula representation of multiv...

We develop an integration by parts technique for point processes, with application to the computation of sensitivities via Monte Carlo simulations in stochastic models with jumps. The method is applied to density estimation and to the construction of a modified kernel estimator which is less sensitive to variations of the bandwidth parameter than standard kernel estimators. Simulations are pres...

Lusifer is a Monte Carlo event generator for all processes ee → 6 fermions, which is based on the multi-channel Monte Carlo integration technique and employs the full set of tree-level diagrams. External fermions are taken to be massless, but can be arbitrarily polarized. The calculation of the helicity amplitudes and of the squared matrix elements is presented in a compact way. Initial-state r...