A sequential dynamic Bayesian network for pore-pressure estimation with uncertainty quantification
نویسندگان
چکیده
منابع مشابه
A Bayesian Markov-switching Model for Sparse Dynamic Network Estimation
Inferring Dynamic Bayesian Networks (DBNs) from multivariate time series data is a key step towards the understanding of complex systems as it reveals important dependency relationship underlying such systems. Most of the traditional approaches assume a “static” DBN. Yet in many relevant applications, such as those arising in biology and social sciences, the dependency structures may vary over ...
متن کاملBayesian Solution Uncertainty Quantification for Differential Equations
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty can avoid posterior under-coverage when likelihoods are constructed from a coarsely discretized approximation to system equations. A formalism is proposed for inferring a fixed but a priori unknown model trajectory thr...
متن کاملBayesian dynamic modelling for nonstationary hydroclimatic time series forecasting along with uncertainty quantification
Forecasting of hydrologic time series, with the quantification of uncertainty, is an important tool for adaptive water resources management. Nonstationarity, caused by climate forcing and other factors, such as change in physical properties of catchment (urbanization, vegetation change, etc.), makes the forecasting task too difficult to model by traditional Box–Jenkins approaches. In this paper...
متن کاملEstimation of pore water pressure in the body of earth dams during construction with intelligent models
One of the basic measures in managing the stability of earth dams is to accurately estimate the amount of pore water pressure in the body of the dam during and after its construction. In this study, three different models of artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and gene expression programming (GEP) to estimate the pore water pressure in the body of Kab...
متن کاملA comparison of two Bayesian approaches for uncertainty quantification
Statistical calibration of model parameters conditioned on observations is performed in a Bayesian framework by evaluating the joint posterior probability density function (pdf) of the parameters. The posterior pdf is very often inferred by sampling the parameters with Markov Chain Monte Carlo (MCMC) algorithms. Recently, an alternative technique to calculate the socalled Maximal Conditional Po...
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: GEOPHYSICS
سال: 2018
ISSN: 0016-8033,1942-2156
DOI: 10.1190/geo2016-0566.1