This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The Inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
FISMAT accommodates different arithmetic operators, fuzzification and defuzzification algorithm, implication relations, and different method of approximate reasoning such as Compositional Rule of Inference (CRI) and Approximate Analogical Reasoning Scheme based on Similarity Measure.
ReBEL is a Matlabtoolkit of functions and scripts, designed to
facilitate sequential Bayesian Inference (estimation) in general state
space models. This software consolidates research on new methods for
recursive Bayesian estimation and Kalman filtering by Rudolph van der
Merwe and Eric A. Wan. The code is developed and maintained by Rudolph
van der Merwe at the OGI School of Science & Engineering at OHSU
(Oregon Health & Science University).
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical Inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
This template file is used to completely describe a system in a generalized
％ state space format useable by the ReBEL Inference and estimation system.
％ This file must be copied, renamed and adapted to your specific problem. The
％ interface to each function should NOT BE CHANGED however.
Accurate estimates of the autocorrelation or power spectrum can be obtained with a parametric model (AR, MA or ARMA). With automatic Inference, not only the model parameters but also the model structure are determined from the data. It is assumed that the ARMASA toolbox is presen
The Fuzzy Logic Toolbox™ product extends the MATLAB® technical computing environment with tools for designing systems based on fuzzy logic. Graphical user interfaces (GUIs) guide you through the steps of fuzzy Inference system design. Functions are provided for many common fuzzy logic methods, including fuzzy clustering and adaptive neurofuzzy learning.
Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
past ten years. In particular, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical models have emerged as a general framework
for describing and applying probabilistic models. Also, the practical applicability of
Bayesian methods has been greatly enhanced through the development of a range of
approximate Inference algorithms such as variational Bayes and expectation propa-
gation. Similarly, new models based on kernels have had significant impact on both
algorithms and applications.
