approximate Bayesian inferencehigh-dimensional dataGaussian process classifierstochastic optimizationIn this paper, we adopt a Bayesian point of view, based on Gaussian processes, for classifying high-dimensional data. Since computing the exact marginal likelihood remains difficult, if not impossible, for ...
A different approach, based on a-priori estimation using Bayesian inference, has been proposed by Traiola et al. [25, 26]. Selecting a particular approximate configuration of a given application, generated by a given technique, is a major challenge. AxC techniques, in facts, may generate ...
Statistical methods of inference typically require the likelihood function to be computable\\udin a reasonable amount of time. The class of "likelihood-free" methods termed Approximate\\udBayesian Computation (ABC) is able to eliminate t... DJ Lawson 被引量: 8发表: 2010年 Retrospective sampling...
In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly... K Cranmer,J Pavez,G Louppe 被引量: 49发表: 2015年 Approximate Reasoning Schemes: Classifiers for Computing with Words In the paper we...
In Ref. [21], the ictal EEG signals are classified efficiently using an adaptive neuro-fuzzy inference system by importing the extracted distinct features from the Hilbert transformed EEG signals. Parija et al. [22] propose the combination of EMD and weighted multi-kernel random vector functional...
Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu accelera- tion. arXiv preprint arXiv:1809.11165, 2018. [15] Weijun Hong, Guilin Li, Weinan Zhang, Ruiming Tang, Yunhe Wang, Zhenguo Li, and Yong Yu. Dropnas: Grouped operation dropout for differe...
This chapter proposes a generative model and a Bayesian learning scheme for a classifier that takes uncertainty at all levels of inference into account. Classifier inputs will be uncertain if they are estimated, for example using a preprocessing method. Classical approaches would neglect the ...
The resulting models are useful for classification as well as more general inference tasks. Our methods have been demonstrated to yield strong performance in comparison with alternative models such as Bayesian networks, dependence trees, and multilayer perceptrons. In this work we provide insight into ...
Algorithms for approximate probability propagation in Bayesian networks - Cano, Moral, et al. - 2004 () Citation Context ...ms to be solved, but it also showed that inference in Bayesian networks was #P-complete [Cooper, 1990]. This lead to the development of approximate algorithms, mainly ...
This is the Editorial article summarizing the scope of the Special Issue: Approximate Bayesian Inference. Keywords: Bayesian statistics; machine learning; variational approximations; PAC-Bayes; expectation-propagation; Markov chain Monte Carlo; Langevin Monte Carlo; sequential Monte Carlo; Laplace approximati...