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About 10,000 papers for “Sample space”

Sample Space

Wiley StatsRef: Statistics Reference Online · 2014

Cited by 0

Primary Sample Space Path Guiding

Jerry Jinfeng Guo, Pablo Bauszat, Jacco Bikker et al. · Data Archiving and Networked Services (DANS) · 2018

Guiding path tracing in light transport simulation has been one of the practical choices for variance reduction in production rendering. For this purpose, typically structures in the spatial-directional domain are built. We present a novel scheme for unbiased path guiding. Different from existing methods, we work in primary sample space. We collect records of primary samples as well as the luminance that the resulting path contributes and build a multiple dimensional structure, from which we derive random numbers that are fed into the path tracer. This scheme is executed completely outside the rendering kernel. We demonstrate that this method is practical and efficient. We manage to reduce variance and zero radiance paths by only working in the primary sample space.

Cited by 19Open access

Sample space in probability theory

Luis Miguel Angel Vargas Bolivar, Claudia Ximena Carvajal Saavedra, Ruben Jose Rodriguez Grajales · Repositorio Universidad Francisco de Paula Santander · 2022

El espacio muestral o espacio de muestreo es un conjunto de todos los resultados posibles individuales de un experimento aleatorio, esta nos sirve para calcular la frecuencia con que se obtiene los resultados de una experiencia aleatoria. En casos equiprobables, la probabilidad se calcula dividiendo la cantidad de casos favorables por la cantidad de casos posibles.

Cited by 0Open access

Space X1 First Entry Sample

John T. James · NASA STI Repository (National Aeronautics and Space Administration) · 2012

One mini-grab sample container (m-GSC) was returned aboard Space X1 because of the importance of quickly knowing first-entry conditions in this new commercial module. This sample was analyzed alongside samples of the portable clean room (PCR) used in the Space X complex at KSC. The recoveries of C-13-acetone, fluorobenzene, and chlorobenzene from the GSCs averaged 130, 129, and 132 %, respectively.

Cited by 0Open access

Learning to Importance Sample in Primary Sample Space

Quan Zheng, Matthias Zwicker · Computer Graphics Forum · 2019

Abstract Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. We propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene‐dependent training phase, we learn to generate samples with a desired density in the primary sample space of the renderer using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real‐valued non‐volume preserving (“Real NVP”) transformations in high dimensional spaces. We use Real NVP to non‐linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with an existing rendering technique by treating it as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.

Cited by 41

Learning to Importance Sample in Primary Sample Space

Quan Zheng, Matthias Zwicker · arXiv (Cornell University) · 2018

Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. In this paper, we propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the rendering algorithm using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real-valued non-volume preserving ('Real NVP') transformations in high dimensional spaces. We use Real NVP to non-linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with many existing rendering techniques by treating them as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.

Cited by 6Open access

Mapping the global Mobile Space, The nomadic Space as sample

RETAILLÉ, Denis · HAL (Le Centre pour la Communication Scientifique Directe) · 1999

Reprise écrite d'une communication ancienne (1999) non publiée montrant comment la cartographie de l'espace nomade conduit à penser un espace mobile lui-même utile pour la figuration des phénomènes de la mondialisation.

Cited by 0

Algorithmic Probability on Continuous Sample Space

Summary Of The, To R Satisfying · 2007

he material presented is based mainly on [1] though a few corrections and remarks were added. 2 Continuous Measures Let us rst remind several denitions from the previous material. Let B be a nite set of basic elements. For example, B = f0; 1g. A function : B ! R denes a probability measure if () = 1, (x) = P b2B (xb). A semimeasure is a defective measure. A function : B ! R denes a semimeasure if () 1, 1 (x) P b2B (xb). We

Cited by 0