The estimation of cosmological parameters from precision observables is an important industry with crucial ramifications for particle physics. This article discusses the statistical methods presently used in cosmological data analysis, highlighting the main assumptions and uncertainties. The topics covered are parameter estimation, model selection, multi-model inference, and experimental design...

Inference in popular nonparametric Bayesian models typically relies on sampling or other approximations. This paper presents a general methodology for constructing novel tractable nonparametric Bayesian methods by applying the kernel trick to inference in a parametric Bayesian model. For example, Gaussian process regression can be derived this way from Bayesian linear regression. Despite the su...

acceptance sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. a number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. the acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are acco...

Stochastic inverse problems in heat conduction with consideration of uncertainties in the measured temperature data, temperature sensor locations and thermophysical properties are addressed using a Bayesian statistical inference method. Both parameter estimation and thermal history reconstruction problems, including boundary heat flux and heat source reconstruction, are studied. Probabilistic s...

A Bayesian Exponential Family Harmonium (BEFH) model is presented for topical modeling of text and multimedia data, and for “posterior” latent semantic projection of such data for subsequent data mining tasks. BEFHs are a Bayesian approach to inference and learning with the recently proposed EFH models and their variants, which enables smoothed, robust estimation of the topicattribute coupling ...

Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...

Data privacy issues frequently and increasingly arise for data sharing and data analysis tasks. In this paper, we study the problem of online query answering under the rigorous differential privacy model. The existing interactive mechanisms for differential privacy can only support a limited number of queries before the accumulated cost of privacy reaches a certain bound. This limitation has gr...

Evaporation phenomena is a effective climate component on water resources management and has special importance in agriculture. In this paper, Bayesian belief networks (BBNs) as a non-linear modeling technique provide an evaporation estimation  method under uncertainty. As a case study, we estimated the surface water evaporation of the Persian Gulf and worked with a dataset of observations ...