investigation of the interrelationships between yield and its components will improve the efficiency of a breeding program with appropriate selection criteria. in this study, the relationship among yield components and their direct and indirect influences on the total yield of melon were investigated. the study was based on evaluation of 49 entries gener-ated from a 7×7 diallel involving irania...

When optimizing an accelerated degradation testing (ADT) plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, whe...

uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. the objective of this study is to develop and apply a bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in zidasht catchment, iran....

We consider simulation-based methods for exploration and maximization of expected utility in sequential decision problems. We consider problems which require backward induction with analytically intractable expected utility integrals at each stage. We propose to use forward simulation to approximate the integral expressions, and a reduction of the allowable action space to avoid problems relate...

[1] Two elementary issues in contemporary Earth system science and engineering are (1) the specification of model parameter values which characterize a system and (2) the estimation of state variables which express the system dynamic. This paper explores a novel sequential hydrologic data assimilation approach for estimating model parameters and state variables using particle filters (PFs). PFs...

Approximate Bayesian Computational (ABC) methods, or likelihood-free methods, have appeared in the past fifteen years as useful methods to perform Bayesian analysis when the likelihood is analytically or computationally intractable. Several ABC methods have been proposed: MCMC methods have been developed by Marjoram et al. [2003] and by Bortot et al. [2007] for instance, and sequential methods ...

In this article, a general problem of sequential statistical inference for general discrete-time stochastic processes is considered. The problem is to minimize an average sample number given that Bayesian risk due to incorrect decision does not exceed some given bound. We characterize the form of optimal sequential stopping rules in this problem. In particular, we have a characterization of the...

We provide a brief overview of the state-of-the-art in quickest (sequential) changepoint detection and present some new results on asymptotic and numerical analysis of main competitors such as the CUSUM, Shiryaev–Roberts, and Shiryaev detection procedures in a Bayesian context.

There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data. While sequential update of parameters for a fixed structure can be accomplished using standard techniques, sequential update of network struct...

Basic principles of Bayesian networks, inference with them and discovery of Bayesian network structures are briefly introduced. Then, the applicability of these methods to the analysis of process data is addressed. The case study problems involve mining of dependencies from training data and using the discovered dependency models for prediction of quality indicator values. Prediction results ar...