Expectation propagation in linear regression models with spike-and-slab priors
نویسندگان
چکیده
منابع مشابه
Convergent Expectation Propagation in Linear Models with Spike-and-slab Priors
Exact inference in the linear regression model with spike and slab priors is often intractable. Expectation propagation (EP) can be used for approximate inference. However, the regular sequential form of EP (R-EP) may fail to converge in this model when the size of the training set is very small. As an alternative, we propose a provably convergent EP algorithm (PC-EP). PC-EP is proved to minimi...
متن کاملOnline Spike-and-slab Inference with Stochastic Expectation Propagation
We present OLSS, an online algorithm for Bayesian spike-and-slab model inference, based on the recently proposed stochastic Expectation Propagation (SEP) framework [7]. We use a fully factorized form to efficiently process high dimensional features; further, we extend the SEP framework by incorporating multiple approximate average likelihoods, each of which corresponds to a cluster of samples (...
متن کاملGeneralized spike-and-slab priors for Bayesian group feature selection using expectation propagation
We describe a Bayesian method for group feature selection in linear regression problems. The method is based on a generalized version of the standard spike-and-slab prior distribution which is often used for individual feature selection. Exact Bayesian inference under the prior considered is infeasible for typical regression problems. However, approximate inference can be carried out efficientl...
متن کاملExpectation Propagation for Rectified Linear Poisson Regression
The Poisson likelihood with rectified linear function as non-linearity is a physically plausible model to discribe the stochastic arrival process of photons or other particles at a detector. At low emission rates the discrete nature of this process leads to measurement noise that behaves very differently from additive white Gaussian noise. To address the intractable inference problem for such m...
متن کاملRobust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
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: Machine Learning
سال: 2014
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-014-5475-7