Distributed Gibbs: A Linear-Space Sampling-Based DCOP Algorithm

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Distributed Gibbs: a memory-bounded sampling-based DCOP algorithm

Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the numb...

متن کامل

GD-GIBBS: a GPU-based sampling algorithm for solving distributed constraint optimization problems

Researchers have recently introduced a promising new class of Distributed Constraint Optimization Problem (DCOP) algorithms that is based on sampling. This paradigm is very amenable to parallelization since sampling algorithms require a lot of samples to ensure convergence, and the sampling process can be designed to be executed in parallel. This paper presents GPU-based D-Gibbs (GD-Gibbs), whi...

متن کامل

Asynchronous Distributed Gibbs Sampling (Preprint Version 0.1)

Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences. Since MCMC methods typically suffer from poor scaling when the integral in question is high-dimensional (for example, in problems in Bayesian statistics involving large data sets), researchers have attempted to find ways to speed ...

متن کامل

On Lifting the Gibbs Sampling Algorithm

First-order probabilistic models combine the power of first-order logic, the de facto tool for handling relational structure, with probabilistic graphical models, the de facto tool for handling uncertainty. Lifted probabilistic inference algorithms for them have been the subject of much recent research. The main idea in these algorithms is to improve the accuracy and scalability of existing gra...

متن کامل

A General Gibbs Sampling Algorithm for Analyzing Linear Models Using the Sas System

A general Gibbs sampling algorithm for analyzing a broad class of linear models under a Bayesian framework is presented using Markov Chain Monte Carlo (MCMC) methodology in the SAS system. The analysis of a North Central Cancer Treatment Group (NCCTG) oncology clinical trial involving a two-period two-treatment crossover design is presented as an example. Results for the Bayesian model are comp...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Journal of Artificial Intelligence Research

سال: 2019

ISSN: 1076-9757

DOI: 10.1613/jair.1.11400