Fiducial and Posterior Sampling
نویسندگان
چکیده
منابع مشابه
Posterior Sampling with Improved Eeciency
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of model realizations that sample the posterior probability distribution of a Bayesian analysis. That sequence may be used to make inferences about the model uncertainties that derive from measurement uncertainties. This paper presents an approach to improving the eeciency of the Metropolis approach to ...
متن کاملPosterior sampling with improved efficiency
The Markov Chain Monte Carlo (MCMC) technique provides a means to generate a random sequence of model realizations that sample the posterior probability distribution of a Bayesian analysis. That sequence may be used to make inferences about the model uncertainties that derive from measurement uncertainties. This paper presents an approach to improving the efficiency of the Metropolis approach t...
متن کاملPosterior Sampling of Scientific Images
Scientific image processing involves a variety of problems including imagemodelling, reconstruction, and synthesis.We are collaborating on an imaging problem in porous media, studied in-situ in an imagingMRI in which it is imperative to infer aspects of the porous sample at scales unresolved by the MRI. In this paper we develop an MCMC approach to resolution enhancement, where a low-resolution ...
متن کاملApproximate Slice Sampling for Bayesian Posterior Inference
In this paper, we advance the theory of large scale Bayesian posterior inference by introducing a new approximate slice sampler that uses only small mini-batches of data in every iteration. While this introduces a bias in the stationary distribution, the computational savings allow us to draw more samples in a given amount of time and reduce sampling variance. We empirically verify on three dif...
متن کاملRenyi Differential Privacy Mechanisms for Posterior Sampling
With the newly proposed privacy definition of Rényi Differential Privacy (RDP) in [15], we re-examine the inherent privacy of releasing a single sample from a posterior distribution. We exploit the impact of the prior distribution in mitigating the influence of individual data points. In particular, we focus on sampling from an exponential family and specific generalized linear models, such as ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications in Statistics - Theory and Methods
سال: 2015
ISSN: 0361-0926,1532-415X
DOI: 10.1080/03610926.2013.823207