An open source Bayesian Monte Carlo isotope mixing model with applications in Earth surface processes
نویسندگان
چکیده
منابع مشابه
Multiple Monte Carlo testing, with applications in spatial point processes
The rank envelope test (Myllymäki et al., Global envelope tests for spatial processes, arXiv:1307.0239 [stat.ME]) is proposed as a solution to multiple testing problem for Monte Carlo tests. Three different situations are recognized: 1) a few univariate Monte Carlo tests, 2) a Monte Carlo test with a function as the test statistic, 3) several Monte Carlo tests with functions as test statistics....
متن کاملDistributed Evolutionary Monte Carlo with Applications to Bayesian Analysis
Sampling from multimodal and high dimensional target distribution posits a great challenge in Bayesian analysis. This paper combines the attractive features of the distributed genetic algorithm and the Markov Chain Monte Carlo, resulting in a new Monte Carlo algorithm Distributed Evolutionary Monte Carlo (DEMC) for real-valued problems. DEMC evolves a population of the Markov chains through gen...
متن کاملBayesian Model Comparison by Monte Carlo Chaining
The techniques of Bayesian inference have been applied with great success to many problems in neural computing including evaluation of regression functions, determination of error bars on predictions, and the treatment of hyper-parameters. However, the problem of model comparison is a much more challenging one for which current techniques have significant limitations. In this paper we show how ...
متن کاملHybrid Monte Carlo with Chaotic Mixing
We propose a hybrid Monte Carlo (HMC) technique applicable to high-dimensional multivariate normal distributions that effectively samples along chaotic trajectories. The method is predicated on the freedom of choice of the HMC momentum distribution, and due to its mixing properties, exhibits sample-to-sample autocorrelations that decay far faster than those in the traditional hybrid Monte Carlo...
متن کاملBayesian Monte Carlo
We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation of prior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method. We also attempt more challenging multidimensional integrals involved in computi...
متن کاملذخیره 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: Geochemistry, Geophysics, Geosystems
سال: 2015
ISSN: 1525-2027
DOI: 10.1002/2014gc005683