The Experts below are selected from a list of 2055 Experts worldwide ranked by ideXlab platform

J.a. Gubner - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - Nonparametric estimation of interaction functions for two-type pairwise interaction point Processes
    2001 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.01CH37221), 2001
    Co-Authors: J.a. Gubner, Wei-bin Chang
    Abstract:

    Nonparametric estimation of interaction functions for two-type pairwise interaction point Processes is addressed. Such a problem is known to be challenging due to the intractable normalizing constant present in the density function. It is shown that the means of the marked interpoint distance functions embedded in the two-type pairwise interaction point Process converge to the means of an Inhomogeneous Poisson Process. This suggests a simple and effective nonparametric estimation method. An example is presented to illustrate the efficacy of our method. Our results can be generalized to multitype point Processes in a straightforward manner, although the notation is more involved.

  • Poisson limits and nonparametric estimation for pairwise interaction point Processes
    Journal of Applied Probability, 2000
    Co-Authors: Wei-bin Chang, J.a. Gubner
    Abstract:

    The distribution of the interpoint distance Process of a sequence of pairwise interaction point Processes is considered. It is shown that, if the interaction function is piecewise-continuous, then the sequence of interpoint distance Processes converges weakly to an Inhomogeneous Poisson Process under certain sparseness conditions. Convergence of the expectation of the interpoint distance Process to the mean of the limiting Poisson Process is also established. This suggests a new nonparametric estimator for the interaction function if independent identically distributed samples of the point Process are available.

  • Blind intensity estimation from shot-noise data
    IEEE Transactions on Signal Processing, 1997
    Co-Authors: R.e. Sequeira, J.a. Gubner
    Abstract:

    The estimation of the intensity function of an Inhomogeneous Poisson Process is considered when the observable data consists of sampled shot noise that results from passing the Poisson Process through an unknown linear time-invariant system. The proposed method consists of first estimating a histogram of the underlying point Process. The estimated histogram is used to construct a kernel estimate of the intensity function. An estimate of the unknown impulse response of the linear time-invariant system is constructed via a regularized backsubstitution of a discrete-time convolution with the estimated histogram.

Tien-hsiang Chang - One of the best experts on this subject based on the ideXlab platform.

  • An optimal replacement period for a k-out-of-n: F system subject to shocks
    International Journal of Systems Science, 2001
    Co-Authors: Shey-huei Sheu, Tien-hsiang Chang
    Abstract:

    A k-out-of-n: F system, which consists of n components and fails if at least k of the n components fail, is subject to shocks that arrive according to an Inhomogeneous Poisson Process. The k-out-of-n: F system is completely replaced (planned replacement) whenever it reaches age T (T > 0) at a fixed cost R2. If the mth shock arrives at age Sm < T, it causes the simultaneous failure of j components at the same time with probability pj

Zoubin Ghahramani - One of the best experts on this subject based on the ideXlab platform.

  • KDD - Discovering latent influence in online social activities via shared cascade Poisson Processes
    Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 2013
    Co-Authors: Tomoharu Iwata, Amar Shah, Zoubin Ghahramani
    Abstract:

    Many people share their activities with others through online communities. These shared activities have an impact on other users' activities. For example, users are likely to become interested in items that are adopted (e.g. liked, bought and shared) by their friends. In this paper, we propose a probabilistic model for discovering latent influence from sequences of item adoption events. An Inhomogeneous Poisson Process is used for modeling a sequence, in which adoption by a user triggers the subsequent adoption of the same item by other users. For modeling adoption of multiple items, we employ multiple Inhomogeneous Poisson Processes, which share parameters, such as influence for each user and relations between users. The proposed model can be used for finding influential users, discovering relations between users and predicting item popularity in the future. We present an efficient Bayesian inference procedure of the proposed model based on the stochastic EM algorithm. The effectiveness of the proposed model is demonstrated by using real data sets in a social bookmark sharing service.

Shey-huei Sheu - One of the best experts on this subject based on the ideXlab platform.

  • An optimal replacement period for a k-out-of-n: F system subject to shocks
    International Journal of Systems Science, 2001
    Co-Authors: Shey-huei Sheu, Tien-hsiang Chang
    Abstract:

    A k-out-of-n: F system, which consists of n components and fails if at least k of the n components fail, is subject to shocks that arrive according to an Inhomogeneous Poisson Process. The k-out-of-n: F system is completely replaced (planned replacement) whenever it reaches age T (T > 0) at a fixed cost R2. If the mth shock arrives at age Sm < T, it causes the simultaneous failure of j components at the same time with probability pj

Eva B. Vedel Jensen - One of the best experts on this subject based on the ideXlab platform.

  • Bayesian analysis of spatial point Processes in the neighbourhood of Voronoi networks
    Statistics and Computing, 2007
    Co-Authors: Øivind Skare, Jesper Møller, Eva B. Vedel Jensen
    Abstract:

    A model for an Inhomogeneous Poisson Process with high intensity near the edges of a Voronoi tessellation in 2D or 3D is proposed. The model is analysed in a Bayesian setting with priors on nuclei of the Voronoi tessellation and other model parameters. An MCMC algorithm is constructed to sample from the posterior, which contains information about the unobserved Voronoi tessellation and the model parameters. A major element of the MCMC algorithm is the reconstruction of the Voronoi tessellation after a proposed local change of the tessellation. A simulation study and examples of applications from biology (animal territories) and material science (alumina grain structure) are presented.