Mostrando recursos 1 - 20 de 30

  1. A Bayesian approach to extended models for exceedance

    Ferraz do Nascimento, Fernando; Bourguignon Pereira, Marcelo
    In extreme value theory, the generalized Pareto distribution (GPD) is a family of continuous distribution used to model the tail of the distribution to values higher than a threshold $u$. Several works have used this method to approximate the tail of distribution. In this paper, we propose two extensions of GPD, including an additional shape parameter, to provide a more flexible distribution for exceedance. Some properties of these approximations are presented. Inference for these extensions were performed under the Bayesian paradigm, and the results showed fit improvement when compared with the standard GPD in applications to environmental and financial data.

  2. A Bayesian approach to extended models for exceedance

    Ferraz do Nascimento, Fernando; Bourguignon Pereira, Marcelo
    In extreme value theory, the generalized Pareto distribution (GPD) is a family of continuous distribution used to model the tail of the distribution to values higher than a threshold $u$. Several works have used this method to approximate the tail of distribution. In this paper, we propose two extensions of GPD, including an additional shape parameter, to provide a more flexible distribution for exceedance. Some properties of these approximations are presented. Inference for these extensions were performed under the Bayesian paradigm, and the results showed fit improvement when compared with the standard GPD in applications to environmental and financial data.

  3. A Bayesian approach to extended models for exceedance

    Ferraz do Nascimento, Fernando; Bourguignon Pereira, Marcelo
    In extreme value theory, the generalized Pareto distribution (GPD) is a family of continuous distribution used to model the tail of the distribution to values higher than a threshold $u$. Several works have used this method to approximate the tail of distribution. In this paper, we propose two extensions of GPD, including an additional shape parameter, to provide a more flexible distribution for exceedance. Some properties of these approximations are presented. Inference for these extensions were performed under the Bayesian paradigm, and the results showed fit improvement when compared with the standard GPD in applications to environmental and financial data.

  4. Studying the effective brain connectivity using multiregression dynamic models

    Costa, Lilia; Nichols, Thomas; Smith, Jim Q.
    The Multiregression Dynamic Model (MDM) is a multivariate graphical model for a multidimensional time series that allows the estimation of time-varying effective connectivity. An MDM is a state space model where connection weights reflect the contemporaneous interactions between brain regions. Because the marginal likelihood has a closed form, model selection across a large number of potential connectivity networks is easy to perform. With application of the Integer Programming Algorithm, we can quickly find optimal models that satisfy acyclic graph constraints and, due to a factorisation of the marginal likelihood, the search over all possible directed (acyclic or cyclic) graphical structures...

  5. Studying the effective brain connectivity using multiregression dynamic models

    Costa, Lilia; Nichols, Thomas; Smith, Jim Q.
    The Multiregression Dynamic Model (MDM) is a multivariate graphical model for a multidimensional time series that allows the estimation of time-varying effective connectivity. An MDM is a state space model where connection weights reflect the contemporaneous interactions between brain regions. Because the marginal likelihood has a closed form, model selection across a large number of potential connectivity networks is easy to perform. With application of the Integer Programming Algorithm, we can quickly find optimal models that satisfy acyclic graph constraints and, due to a factorisation of the marginal likelihood, the search over all possible directed (acyclic or cyclic) graphical structures...

  6. Studying the effective brain connectivity using multiregression dynamic models

    Costa, Lilia; Nichols, Thomas; Smith, Jim Q.
    The Multiregression Dynamic Model (MDM) is a multivariate graphical model for a multidimensional time series that allows the estimation of time-varying effective connectivity. An MDM is a state space model where connection weights reflect the contemporaneous interactions between brain regions. Because the marginal likelihood has a closed form, model selection across a large number of potential connectivity networks is easy to perform. With application of the Integer Programming Algorithm, we can quickly find optimal models that satisfy acyclic graph constraints and, due to a factorisation of the marginal likelihood, the search over all possible directed (acyclic or cyclic) graphical structures...

  7. A Bayesian approach for a zero modified Poisson model to predict match outcomes applied to the 2012–13 La Liga season

    Conceição, Katiane S.; Suzuki, Adriano K.; Andrade, Marinho G.; Louzada, Francisco
    In any sports competition, strong interest is devoted to the knowledge on the team that will be champion. The result of a match, the chance of a team either qualifying for a specific tournament, or relegating, the best attack and defense are all topics of interest. This paper presents a Bayesian methodology for modeling the number of goals scored by a team based on Zero-Modified Poisson distribution. An important advantage of this distribution is the flexibility in modeling count data without previous knowledge of the sampling characteristic with respect to the frequency of zeros (inflated, standard, deflation). These characteristics are...

  8. A Bayesian approach for a zero modified Poisson model to predict match outcomes applied to the 2012–13 La Liga season

    Conceição, Katiane S.; Suzuki, Adriano K.; Andrade, Marinho G.; Louzada, Francisco
    In any sports competition, strong interest is devoted to the knowledge on the team that will be champion. The result of a match, the chance of a team either qualifying for a specific tournament, or relegating, the best attack and defense are all topics of interest. This paper presents a Bayesian methodology for modeling the number of goals scored by a team based on Zero-Modified Poisson distribution. An important advantage of this distribution is the flexibility in modeling count data without previous knowledge of the sampling characteristic with respect to the frequency of zeros (inflated, standard, deflation). These characteristics are...

  9. A Bayesian approach for a zero modified Poisson model to predict match outcomes applied to the 2012–13 La Liga season

    Conceição, Katiane S.; Suzuki, Adriano K.; Andrade, Marinho G.; Louzada, Francisco
    In any sports competition, strong interest is devoted to the knowledge on the team that will be champion. The result of a match, the chance of a team either qualifying for a specific tournament, or relegating, the best attack and defense are all topics of interest. This paper presents a Bayesian methodology for modeling the number of goals scored by a team based on Zero-Modified Poisson distribution. An important advantage of this distribution is the flexibility in modeling count data without previous knowledge of the sampling characteristic with respect to the frequency of zeros (inflated, standard, deflation). These characteristics are...

  10. Barker’s algorithm for Bayesian inference with intractable likelihoods

    Gonçalves, Flávio B.; Łatuszyński, Krzysztof; Roberts, Gareth O.
    In this expository paper, we abstract and describe a simple MCMC scheme for sampling from intractable target densities. The approach has been introduced in Gonçalves, Łatuszyński and Roberts (2017a) in the specific context of jump-diffusions, and is based on the Barker’s algorithm paired with a simple Bernoulli factory type scheme, the so called 2-coin algorithm. In many settings, it is an alternative to standard Metropolis–Hastings pseudo-marginal method for simulating from intractable target densities. Although Barker’s is well known to be slightly less efficient than Metropolis–Hastings, the key advantage of our approach is that it allows to implement the “marginal Barker’s”...

  11. Barker’s algorithm for Bayesian inference with intractable likelihoods

    Gonçalves, Flávio B.; Łatuszyński, Krzysztof; Roberts, Gareth O.
    In this expository paper, we abstract and describe a simple MCMC scheme for sampling from intractable target densities. The approach has been introduced in Gonçalves, Łatuszyński and Roberts (2017a) in the specific context of jump-diffusions, and is based on the Barker’s algorithm paired with a simple Bernoulli factory type scheme, the so called 2-coin algorithm. In many settings, it is an alternative to standard Metropolis–Hastings pseudo-marginal method for simulating from intractable target densities. Although Barker’s is well known to be slightly less efficient than Metropolis–Hastings, the key advantage of our approach is that it allows to implement the “marginal Barker’s”...

  12. Barker’s algorithm for Bayesian inference with intractable likelihoods

    Gonçalves, Flávio B.; Łatuszyński, Krzysztof; Roberts, Gareth O.
    In this expository paper, we abstract and describe a simple MCMC scheme for sampling from intractable target densities. The approach has been introduced in Gonçalves, Łatuszyński and Roberts (2017a) in the specific context of jump-diffusions, and is based on the Barker’s algorithm paired with a simple Bernoulli factory type scheme, the so called 2-coin algorithm. In many settings, it is an alternative to standard Metropolis–Hastings pseudo-marginal method for simulating from intractable target densities. Although Barker’s is well known to be slightly less efficient than Metropolis–Hastings, the key advantage of our approach is that it allows to implement the “marginal Barker’s”...

  13. Dynamics & sparsity in latent threshold factor models: A study in multivariate EEG signal processing

    Nakajima, Jouchi; West, Mike
    We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models,...

  14. Dynamics & sparsity in latent threshold factor models: A study in multivariate EEG signal processing

    Nakajima, Jouchi; West, Mike
    We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models,...

  15. Dynamics & sparsity in latent threshold factor models: A study in multivariate EEG signal processing

    Nakajima, Jouchi; West, Mike
    We discuss Bayesian analysis of multivariate time series with dynamic factor models that exploit time-adaptive sparsity in model parametrizations via the latent threshold approach. One central focus is on the transfer responses of multiple interrelated series to underlying, dynamic latent factor processes. Structured priors on model hyper-parameters are key to the efficacy of dynamic latent thresholding, and MCMC-based computation enables model fitting and analysis. A detailed case study of electroencephalographic (EEG) data from experimental psychiatry highlights the use of latent threshold extensions of time-varying vector autoregressive and factor models. This study explores a class of dynamic transfer response factor models,...

  16. Rejoinder

    Scott, Steven L.

  17. Rejoinder

    Scott, Steven L.

  18. Rejoinder

    Scott, Steven L.

  19. Comment: Consensus Monte Carlo using expectation propagation

    Gelman, Andrew; Vehtari, Aki

  20. Comment: Consensus Monte Carlo using expectation propagation

    Gelman, Andrew; Vehtari, Aki

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