A linear heuristic for multiple importance sampling
Multiple importance sampling combines the probability density functions of several sampling techniques into an importance function. The combination weights are the proportion of samples used for the particular techniques. This paper addresses the determination of the optimal combination weights from...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/22841 |
| Acceso en línea: | http://hdl.handle.net/10256/22841 |
| Access Level: | acceso abierto |
| Palabra clave: | Montecarlo, Mètode de Heurística Monte Carlo method Heuristic Entropia (Teoria de la informació) Entropy (Information theory) Models matemàtics Mathematical models Multiple imputation (Statistics) Imputació múltiple (estadístiques) |
| Sumario: | Multiple importance sampling combines the probability density functions of several sampling techniques into an importance function. The combination weights are the proportion of samples used for the particular techniques. This paper addresses the determination of the optimal combination weights from a few initial samples. Instead of the numerically unstable optimization of the variance, in our solution the quasi-optimal weights are obtained by solving a linear equation, which leads to simpler computations and more robust estimations. The proposed method is validated with 1D numerical examples and with the direct lighting problem of computer graphics |
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