Development of Bayesian Monte Carlo techniques for water quality model uncertainty
نویسندگان
چکیده
منابع مشابه
Monte Carlo Techniques for Bayesian Statistical Inference – A comparative review
In this article, we summariseMonte Carlo simulationmethods commonly used in Bayesian statistical computing. We give descriptions for each algorithm and provide R codes for their implementation via a simple 2-dimensional example. We compare the relative merits of these methods qualitatively by considering their general user-friendliness, and numerically in terms of mean squared error and computa...
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 کاملLimitations of Realistic Monte-Carlo Techniques in Estimating Interval Uncertainty
Because of the measurement errors, the result ỹ = f(x̃1, . . . , x̃n) of processing the measurement results x̃1, . . . , x̃n is, in general, different from the value y = f(x1, . . . , xn) that we would obtain if we knew the exact values x1, . . . , xn of all the inputs. In the linearized case, we can use numerical differentiation to estimate the resulting difference ∆y = ỹ− y; however, this require...
متن کاملComparison of Monte Carlo and Fuzzy Techniques in Uncertainty Modelling
The standard reference in uncertainty modelling is the “Guide to the Expression of Uncertainty in Measurement (GUM)”. GUM groups the occurring uncertain quantities into “Type A” and “Type B”. Uncertainties of “Type A” are determined with the classical statistical methods, while “Type B” is subject to other uncertainties like experience with and knowledge about an instrument. Both types of uncer...
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: Ecological Modelling
سال: 1992
ISSN: 0304-3800
DOI: 10.1016/0304-3800(92)90087-u