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Related papers: MCMC Bayesian Estimation in FIEGARCH Models

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Markov Chain Monte Carlo (MCMC) methods have become a cornerstone of many modern scientific analyses by providing a straightforward approach to numerically estimate uncertainties in the parameters of a model using a sequence of random…

Other Statistics · Statistics 2020-03-10 Joshua S. Speagle

L\'evy processes are widely used in financial mathematics, telecommunication, economics, queueing theory and natural sciences for modelling. We propose an essentially asymptotically efficient estimation method for the system parameters of…

Statistics Theory · Mathematics 2014-04-14 Máté Mánfay , László Gerencsér , Zsanett Orlovits

Markov Chain Monte Carlo (MCMC) sampling is computationally expensive, especially for complex models. Alternative methods make simplifying assumptions about the posterior to reduce computational burden, but their impact on predictive…

Computation · Statistics 2025-10-27 Florian D. van Leeuwen , Sara van Erp

Markov chain Monte Calro methods (MCMC) are commonly used in Bayesian statistics. In the last twenty years, many results have been established for the calculation of the exact convergence rate of MCMC methods. We introduce another rate of…

Statistics Theory · Mathematics 2014-02-17 Kengo Kamatani

In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion (fBM) models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC)…

Computation · Statistics 2022-11-02 Mohamed Maama , Ajay Jasra , Hernando Ombao

We study Bayesian inversion for a model elliptic PDE with unknown diffusion coefficient. We provide complexity analyses of several Markov Chain-Monte Carlo (MCMC) methods for the efficient numerical evaluation of expectations under the…

Numerical Analysis · Mathematics 2013-05-01 Viet Ha Hoang , Christoph Schwab , Andrew M. Stuart

We consider the Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedasticity process, denoted by FIEGARCH(p,d,q), introduced by Bollerslev and Mikkelsen (1996). We present a simulated study regarding the…

Risk Management · Quantitative Finance 2013-05-23 Taiane S. Prass , Sílvia R. C. Lopes

We consider geothermal inverse problems and uncertainty quantification from a Bayesian perspective. Our main goal is to make standard, `out-of-the-box' Markov chain Monte Carlo (MCMC) sampling more feasible for complex simulation models by…

We study Bayesian methods for large-scale linear inverse problems, focusing on the challenging task of hyperparameter estimation. Typical hierarchical Bayesian formulations that follow a Markov Chain Monte Carlo approach are possible for…

Numerical Analysis · Mathematics 2024-01-05 Khalil A Hall-Hooper , Arvind K Saibaba , Julianne Chung , Scot M Miller

We introduce a new Markov-Chain Monte Carlo (MCMC) approach designed for efficient sampling of highly correlated and multimodal posteriors. Parallel tempering, though effective, is a costly technique for sampling such posteriors. Our…

Instrumentation and Methods for Astrophysics · Physics 2014-10-01 Benjamin Farr , Vicky Kalogera , Erik Luijten

Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor. Markov Chain Monte Carlo…

Computation · Statistics 2014-10-23 Jamie Owen , Darren J. Wilkinson , Colin S. Gillespie

Inferring the infinitesimal rates of continuous-time Markov chains (CTMCs) is a central challenge in many scientific domains. This task is hindered by three factors: quadratic growth in the number of rates as the CTMC state space expands,…

Methodology · Statistics 2026-02-09 Filippo Monti , Xiang Ji , Marc A. Suchard

The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are how to carry out exact Bayesian inference and how to account for uncertainty on model parameters when making model-based predictions on…

Machine Learning · Statistics 2014-04-08 Maurizio Filippone , Mark Girolami

Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed…

Computation · Statistics 2020-02-10 Qifan Song , Yan Sun , Mao Ye , Faming Liang

High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g. cancer and normal) from tens of thousands…

Computation · Statistics 2018-07-20 Longhai Li , Weixin Yao

We consider continuous-time diffusion models driven by fractional Brownian motion. Observations are assumed to possess a non-trivial likelihood given the latent path. Due to the non-Markovianity and high-dimensionality of the latent paths,…

Methodology · Statistics 2015-03-25 Alexandros Beskos , Joseph Dureau , Konstantinos Kalogeropoulos

We study the computational complexity of Markov chain Monte Carlo (MCMC) methods for high-dimensional Bayesian linear regression under sparsity constraints. We first show that a Bayesian approach can achieve variable-selection consistency…

Statistics Theory · Mathematics 2015-06-01 Yun Yang , Martin J. Wainwright , Michael I. Jordan

Classical parameter-space Bayesian inference for Bayesian neural networks (BNNs) suffers from several unresolved prior issues, such as knowledge encoding intractability and pathological behaviours in deep networks, which can lead to…

Machine Learning · Computer Science 2024-10-11 Mengjing Wu , Junyu Xuan , Jie Lu

To conduct Bayesian inference with large data sets, it is often convenient or necessary to distribute the data across multiple machines. We consider a likelihood function expressed as a product of terms, each associated with a subset of the…

Computation · Statistics 2020-04-09 Lewis J. Rendell , Adam M. Johansen , Anthony Lee , Nick Whiteley

The Linear Ballistic Accumulator (Brown & Heathcote, 2008) model is used as a measurement tool to answer questions about applied psychology. The analyses based on this model depend upon the model selected and its estimated parameters.…

Methodology · Statistics 2020-03-03 David Gunawan , Guy E. Hawkins , Minh-Ngoc Tran , Robert Kohn , Scott Brown