Back to Search
Start Over
Beyond EM: A faster Bayesian linear regression algorithm without matrix inversions
- Source :
- Neurocomputing. 378:435-440
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- The Bayesian linear regression is a useful tool for many scientific communities. This paper presents a novel algorithm for solving the Bayesian linear regression problem with Gaussian priors, which shares the same spirit as the gradient based methods. In addition, the standard scheme for this task, the Expectation Maximization (EM) algorithm, involves matrix inversions but our proposed algorithm is free of. Numerical experiments demonstrate that the proposed algorithm performs as well as the gradient based and EM algorithms in term of precision, but runs significantly faster than the gradient based and EM algorithms. Due to its matrix-inversion-free nature, the algorithm of this paper is a viable alternative to the competing methods available in the literature.
- Subjects :
- Scheme (programming language)
0209 industrial biotechnology
Computer science
Cognitive Neuroscience
Gaussian
02 engineering and technology
Computer Science Applications
Term (time)
symbols.namesake
Matrix (mathematics)
020901 industrial engineering & automation
Artificial Intelligence
Prior probability
Expectation–maximization algorithm
0202 electrical engineering, electronic engineering, information engineering
symbols
020201 artificial intelligence & image processing
Bayesian linear regression
computer
Algorithm
computer.programming_language
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 378
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........b0af9007bbe25d7f232ed91062f26e39