The Experts below are selected from a list of 8121 Experts worldwide ranked by ideXlab platform

Tongmin Jiang - One of the best experts on this subject based on the ideXlab platform.

  • A life prediction approach based on integrated failure information
    2014 Reliability and Maintainability Symposium, 2014
    Co-Authors: Tongmin Jiang
    Abstract:

    In this paper a Bayesian method is introduced to evaluate products' life by integrating the failure information in field and prior failure information collected from various sources. Calibrators are used to calibrate the difference between failure information in field and prior failure information, then Bayesian approach can be used to integrate failure data. The posterior distributions of unknown parameters can be obtained through Statistical Inference Procedure, which is carried out through Markov chain and Monte Carlo (MCMC) method. Normal distribution and 2-parameters Weibull distribution are discussed based on the fusion model established, and simulation examples are performed to illustrate the use of proposed method. The orthogonal analysis shows the influence of different chosen values to prior means of unknown parameters on prediction results.

  • a bayesian reliability evaluation method with integrated accelerated degradation testing and field information
    Reliability Engineering & System Safety, 2013
    Co-Authors: Lizhi Wang, Xiaoyang Li, Tongmin Jiang
    Abstract:

    Accelerated degradation testing (ADT) is a common approach in reliability prediction, especially for products with high reliability. However, oftentimes the laboratory condition of ADT is different from the field condition; thus, to predict field failure, one need to calibrate the prediction made by using ADT data. In this paper a Bayesian evaluation method is proposed to integrate the ADT data from laboratory with the failure data from field. Calibration factors are introduced to calibrate the difference between the lab and the field conditions so as to predict a product's actual field reliability more accurately. The information fusion and Statistical Inference Procedure are carried out through a Bayesian approach and Markov chain Monte Carlo methods. The proposed method is demonstrated by two examples and the sensitivity analysis to prior distribution assumption.

Bruno Cessac - One of the best experts on this subject based on the ideXlab platform.

  • Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
    Entropy (Basel Switzerland), 2020
    Co-Authors: Rodrigo Cofré, Cesar Maldonado, Bruno Cessac
    Abstract:

    The Thermodynamic Formalism provides a rigorous mathematical framework for studying quantitative and qualitative aspects of dynamical systems. At its core, there is a variational principle that corresponds, in its simplest form, to the Maximum Entropy principle. It is used as a Statistical Inference Procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science. In particular, it has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, in order to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

  • Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
    2020
    Co-Authors: Rodrigo Cofré, Cesar Maldonado, Bruno Cessac
    Abstract:

    The Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle and corresponding, in its simplest form, to the Maximum Entropy principle, used as a Statistical Inference Procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of scienThe Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle and corresponding, in its simplest form, to the Maximum Entropy principle, used as a Statistical Inference Procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science, in particular, has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.ce, in particular, has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

  • Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
    Entropy, 2020
    Co-Authors: Rodrigo Cofré, Cesar Maldonado, Bruno Cessac
    Abstract:

    The Thermodynamic Formalism provides a rigorous mathematical framework to study quantitative and qualitative aspects of dynamical systems. At its core there is a variational principle corresponding, in its simplest form, to the Maximum Entropy principle. It is used as a Statistical Inference Procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science. In particular, it has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

Samuel Soubeyrand - One of the best experts on this subject based on the ideXlab platform.

  • Dating and localizing an invasion from post-introduction data and a coupled reaction–diffusion–absorption model
    Journal of Mathematical Biology, 2019
    Co-Authors: Candy Abboud, Olivier Bonnefon, Eric Parent, Samuel Soubeyrand
    Abstract:

    Invasion of new territories by alien organisms is of primary concern for environmental and health agencies and has been a core topic in mathematical modeling, in particular in the intents of reconstructing the past dynamics of the alien organisms and predicting their future spatial extents. Partial differential equations offer a rich and flexible modeling framework that has been applied to a large number of invasions. In this article, we are specifically interested in dating and localizing the introduction that led to an invasion using mathematical modeling, post-introduction data and an adequate Statistical Inference Procedure. We adopt a mechanistic-Statistical approach grounded on a coupled reaction–diffusion–absorption model representing the dynamics of an organism in an heterogeneous domain with respect to growth. Initial conditions (including the date and site of the introduction) and model parameters related to diffusion, reproduction and mortality are jointly estimated in the Bayesian framework by using an adaptive importance sampling algorithm. This framework is applied to the invasion of Xylella fastidiosa, a phytopathogenic bacterium detected in South Corsica in 2015, France.

  • Dating and localizing an invasion from post-introduction data and a coupled reaction-diffusion-absorption model
    2018
    Co-Authors: Candy Abboud, Olivier Bonnefon, Eric Parent, Samuel Soubeyrand
    Abstract:

    Invasion of new territories by alien organisms is of primary concern for environmental and health agencies and has been a core topic in mathematical modeling, in particular in the intents of reconstructing the past dynamics of the alien organisms and predicting their future spatial extents. Partial differential equations offer a rich and flexible modeling framework that has been applied to a large number of invasions. In this article, we are specifically interested in dating and localizing the introduction that led to an invasion using mathematical modeling, post-introduction data and an adequate Statistical Inference Procedure. We adopt a mechanistic-Statistical approach grounded on a coupled reaction-diffusion-absorption model representing the dynamics of an organism in an heterogeneous domain with respect to growth. Initial conditions (including the date and site of the introduction) and model parameters related to diffusion, reproduction and mortality are jointly estimated in the Bayesian framework by using an adaptive importance sampling algorithm. This framework is applied to the invasion of Xylella fastidiosa, a phytopathogenic bacterium detected in South Corsica in 2015, France.

David H. Zald - One of the best experts on this subject based on the ideXlab platform.

  • IPMI - Exact Topological Inference for Paired Brain Networks via Persistent Homology.
    Information processing in medical imaging : proceedings of the ... conference, 2017
    Co-Authors: Moo K. Chung, Victoria Villalta-gil, Hyekyoung Lee, Paul J. Rathouz, Benjamin B. Lahey, David H. Zald
    Abstract:

    We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology. The framework is general enough for dealing with any type of paired images such as twins, multimodal and longitudinal images. The exact nonparametric Statistical Inference Procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests. The proposed method computes the exact probability in quadratic time while the permutation tests require exponential time. As illustrations, we apply the method to simulated networks and a twin fMRI study. In case of the latter, we determine the Statistical significance of the heritability index of the large-scale reward network where every voxel is a network node.

  • Exact Topological Inference For Paired Brain Networks Via Persistent Homology
    2017
    Co-Authors: Moo K. Chung, Victoria Villalta-gil, Hyekyoung Lee, Paul J. Rathouz, Benjamin B. Lahey, David H. Zald
    Abstract:

    We present a novel framework for characterizing paired brain networks using techniques in hyper-networks, sparse learning and persistent homology. The framework is general enough for dealing with any type of paired images such as twins, multimodal and longitudinal images. The exact nonparametric Statistical Inference Procedure is derived on testing monotonic graph theory features that do not rely on time consuming permutation tests. The proposed method computes the exact probability in quadratic time while the permutation tests require exponential time. As illustrations, we apply the method to simulated networks and a twin fMRI study. In case of the latter, we determine the Statistical significance of the heritability index of the large-scale reward network where every voxel is a network node.

Taras Bodnar - One of the best experts on this subject based on the ideXlab platform.