Study of Preconditioners based on Markov Chain Monte Carlo Methods
نویسنده
چکیده
Nowadays, analysis and design of novel scalable methods and algorithms for fundamental linear algebra problems such as solving Systems of Linear Algebraic Equations with focus on large scale systems is a subject of study. This research focuses on the study of novel mathematical methods and scalable algorithms for computationally intensive problems such as Monte Carlo and Hybrid Methods and Algorithms.
منابع مشابه
Spatial count models on the number of unhealthy days in Tehran
Spatial count data is usually found in most sciences such as environmental science, meteorology, geology and medicine. Spatial generalized linear models based on poisson (poisson-lognormal spatial model) and binomial (binomial-logitnormal spatial model) distributions are often used to analyze discrete count data in which spatial correlation is observed. The likelihood function of these models i...
متن کاملA mixed Bayesian/Frequentist approach in sample size determination problem for clinical trials
In this paper we introduce a stochastic optimization method based ona mixed Bayesian/frequentist approach to a sample size determinationproblem in a clinical trial. The data are assumed to come from a nor-mal distribution for which both the mean and the variance are unknown.In contrast to the usual Bayesian decision theoretic methodology, whichassumes a single decision maker, our method recogni...
متن کاملMonte Carlo Simulation to Solve the Linear Volterra Integral Equations of The Second Kind
This paper is intended to provide a numerical algorithm based on random sampling for solving the linear Volterra integral equations of the second kind. This method is a Monte Carlo (MC) method based on the simulation of a continuous Markov chain. To illustrate the usefulness of this technique we apply it to a test problem. Numerical results are performed in order to show the efficiency and accu...
متن کاملInformation-Geometric Markov Chain Monte Carlo Methods Using Diffusions
Recent work incorporating geometric ideas in Markov chain Monte Carlo is reviewed in order to highlight these advances and their possible application in a range of domains beyond statistics. A full exposition of Markov chains and their use in Monte Carlo simulation for statistical inference and molecular dynamics is provided, with particular emphasis on methods based on Langevin diffusions. Aft...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2015