Displaying similar documents to “Gaussian process modeling with inequality constraints”

Stochastic Inverse Problem with Noisy Simulator. Application to aeronautical model

Nabil Rachdi, Jean-Claude Fort, Thierry Klein (2012)

Annales de la faculté des sciences de Toulouse Mathématiques

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Inverse problem is a current practice in engineering where the goal is to identify parameters from observed data through numerical models. These numerical models, also called Simulators, are built to represent the phenomenon making possible the inference. However, such representation can include some part of variability or commonly called uncertainty (see [4]), arising from some variables of the model. The phenomenon we study is the...

Kalman filter with a non-linear non-Gaussian observation relation.

Tomás Cipra, Asunción Rubio (1991)

Trabajos de Estadística

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The dynamic linear model with a non-linear non-Gaussian observation relation is considered in this paper. Masreliez's theorem (see Masreliez's (1975)) of approximate non-Gaussian filtering with linear state and observation relations is extended to the case of a non-linear observation relation that can be approximated by a second-order Taylor expansion.

Fast and accurate methods of independent component analysis: A survey

Petr Tichavský, Zbyněk Koldovský (2011)

Kybernetika

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This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram...

Robust parameter design using the weighted metric method - The case of 'the smaller the better'

Mostafa Kamali Ardakani, Rassoul Noorossana, Seyed Taghi Akhavan Niaki, Homayoun Lahijanian (2009)

International Journal of Applied Mathematics and Computer Science

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In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable factors optimizing the overall response or outcome. In the cases when a random effects model is applicable and a fixed effects model is assumed instead, an increase in the variance of the coefficient vector should be expected. In this paper, the impacts of this assumption on the results of the experiment in the context of robust...

Transitive geostatistics and statistics per individual : a relevant framework for assessing resources with diffuse limits

Nicolas Bez (2007)

Journal de la société française de statistique

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When assessing marine resources, inferring spatial models has to be performed from a unique realisation. The situations with repetitive surveys that can be considered as repetition of the same regionalized variable are (obviously) rare. In intrinsic geostatistics, this question is usually solved by a couple of key assumptions namely stationarity and ergodicity. Unfortunately, these assumptions and their consequences are often too strong with regards to the reality of fish survey data....

Accelerated Monte Carlo estimation of exceedance probabilities under monotonicity constraints

Nicolas Bousquet (2012)

Annales de la faculté des sciences de Toulouse Mathématiques

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The problem of estimating the probability p = P ( g ( X ) ≤ 0 ) is considered when X represents a multivariate stochastic input of a monotonic function g . First, a heuristic method to bound p , originally proposed by de Rocquigny (2009), is formally described, involving a specialized design of numerical experiments. Then a statistical estimation of p is considered based on a sequential stochastic exploration of the input space. A maximum likelihood estimator of p build from successive dependent Bernoulli...