Bayes inference and regularization Course 04351

نویسنده

  • Mads Nielsen
چکیده

Course 04351 Mads Nielsen January 29, 1999

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Bayesian Sparsity for Intractable Distributions

Bayesian approaches for single-variable and group-structured sparsity outperform L1 regularization, but are challenging to apply to large, potentially intractable models. Here we show how noncentered parameterizations, a common trick for improving the efficiency of exact inference in hierarchical models, can similarly improve the accuracy of variational approximations. We develop this with two ...

متن کامل

A Variational Bayes Approach to Decoding in a Phase-Uncertain Digital Receiver

This paper presents a Bayesian approach to symbol and phase inference in a phase-unsynchronized digital receiver. It primarily extends [10] to the multi-symbol case, using the variational Bayes (VB) approximation to deal with the combinatorial complexity of the phase inference in this case. The work provides a fully Bayesian extension of the EM-based framework underlying current turbo-synchroni...

متن کامل

Bayesian Learning of Sparse Gaussian Graphical Models

Sparse inverse covariance matrix modeling is an important tool for learning relationships among different variables in a Gaussian graph. Most existing algorithms are based on `1 regularization, with the regularization parameters tuned via cross-validation. In this paper, a Bayesian formulation of the problem is proposed, where the regularization parameters are inferred adaptively and cross-vali...

متن کامل

Iterative estimation of reflectivity and image texture: Least-squares migration with an empirical Bayes approach

In many geophysical inverse problems, smoothness assumptions on the underlying geology are used to mitigate the effects of nonuniqueness, poor data coverage, and noise in the data and to improve the quality of the inferred model parameters. Within a Bayesian inference framework, a priori assumptions about the probabilistic structure of the model parameters can impose such a smoothness constrain...

متن کامل

An objective prior that unifies objective Bayes and information-based inference

There are three principle paradigms of statistical inference: (i) Bayesian, (ii) information-based and (iii) frequentist inference [1, 2]. We describe an objective prior (the weighting or w-prior) which unifies objective Bayes and information-based inference. The w-prior is chosen to make the marginal probability an unbiased estimator of the predictive performance of the model. This definition ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1999