Multiple importance sampling revisited: breaking the bounds

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00320743" target="_blank" >RIV/68407700:21230/18:00320743 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1186/s13634-018-0531-2" target="_blank" >https://link.springer.com/article/10.1186/s13634-018-0531-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13634-018-0531-2" target="_blank" >10.1186/s13634-018-0531-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multiple importance sampling revisited: breaking the bounds

Popis výsledku v původním jazyce

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. We revise the proof for this and come to the conclusion that there is no such bound and henceforth it makes sense to look for new estimators that improve on balance heuristic estimator with equal count of samples. Next, we examine a recently introduced non-balance heuristic MIS estimator that is provably better than balance heuristic with equal count of samples, and we improve it both in variance and efficiency. We then obtain an equally provably better one-sample balance heuristic estimator, and finally, we introduce a heuristic for the count of samples that can be used when the individual techniques are biased. All in all, we present three new sampling strategies to improve on both variance and efficiency on the balance heuristic using non-equal count of samples.

Název v anglickém jazyce

Multiple importance sampling revisited: breaking the bounds

Popis výsledku anglicky

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. We revise the proof for this and come to the conclusion that there is no such bound and henceforth it makes sense to look for new estimators that improve on balance heuristic estimator with equal count of samples. Next, we examine a recently introduced non-balance heuristic MIS estimator that is provably better than balance heuristic with equal count of samples, and we improve it both in variance and efficiency. We then obtain an equally provably better one-sample balance heuristic estimator, and finally, we introduce a heuristic for the count of samples that can be used when the individual techniques are biased. All in all, we present three new sampling strategies to improve on both variance and efficiency on the balance heuristic using non-equal count of samples.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA14-19213S" target="_blank" >GA14-19213S: Mobilní měření, komprese a syntéza obrazu pro prostorově proměnnou reflektanci materiálů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

EURASIP Journal on Advances in Signal Processing

ISSN

1687-6180

e-ISSN

1687-6180

Svazek periodika

2018

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

12

Strana od-do

7373-7384

Kód UT WoS článku

000426480300002

EID výsledku v databázi Scopus

2-s2.0-85042676784