Bayesian model based on Markov chain Monte Carlo for identifying mine water sources in Submarine Gold Mining
Authors:
- Bingqian YanBYBingqian Yan
- Fenhua RenFRFenhua Ren
- Meifeng CaiMCMeifeng Cai
- Chen QiaoCQChen Qiao
- 1 April 2020
- journal article
- research article
- Published by Elsevier in Journal of Cleaner Production
- Vol. 253
Abstract
No abstract availableKeywords
Funding Information
- Ministry of Science and Technology of the People's Republic of China
- National Natural Science Foundation of China
This publication has 7 references indexed in Scilit:
- A review of multiple try MCMC algorithms for signal processingDigital Signal Processing, 2018
- Risk Assessment of Water Inrush in an Underground Coal Mine Based on GIS and Fuzzy Set TheoryMine Water and the Environment, 2017
- The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspectivePsychonomic Bulletin & Review, 2017
- Hydrogeological Classification and Water Inrush Accidents in China’s Coal MinesMine Water and the Environment, 2015
- Risk assessment of water inrush in karst tunnels and software developmentArabian Journal of Geosciences, 2014
- Monte Carlo sampling methods using Markov chains and their applicationsBiometrika, 1970
- Monte Carlo Calculations of the Radial Distribution Functions for a Proton?Electron PlasmaAustralian Journal of Physics, 1965