In this paper, we consider some statistical aspects of inverse problems. using Bayesian analysis, particularly estimation and hypothesis-testing questions for parameterdependent differential equations. We relate Bayesian maximum likelihood Io Tikhonw regularization. and we apply the expectatian-minimization (F-M) algorithm to the problem of setting regularization levels. Further, we compare Bay...

Highlights • Orders-of-magnitude improvement in approximate Bayesian inference efficiency • Bitstream autocorrelation limits inference approximation accuracy • Autocorrelation successfully mitigated to improve Bayesian inference approximation • Approximate Bayesian inference efficiently performed in hardware 2 Abstract Advancements in autonomous robotic systems have been impeded by the lack of ...

in today world of internet, it is important to feedback the users based on what they demand. moreover, one of the important tasks in data mining is classification. today, there are several classification techniques in order to solve the classification problems like genetic algorithm, decision tree, bayesian and others. in this article, it is attempted to classify researchers to “expert” and “no...

The application of Bayesian techniques to astronomical data is generally non-trivial because the fitting parameters can be strongly degenerated and formal uncertainties are themselves uncertain. An example provided by contradictory claims over presence or absence a universal acceleration scale ( g † ) in galaxies based on fits rotation curves. To illustrate this we present an analysis which New...

Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines. Based on simple probabilistic models, they render interpretable results and can be embedded in Bayesian frameworks for model selection, feature selection, etc. In this paper, by applying the PAC-Bayesian theorem of Mc...

To formalise our discussion of model uncertainty we will rely on probabilistic modelling, and more specifically on Bayesian modelling. Bayesian probability theory offers us the machinery we need to develop our tools. Together with techniques for approximate inference in Bayesian models, in the next chapter we will present the main results of this work. But prior to that, let us review the main ...