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.
標簽： instantaneous algorithm Bayesian Gaussian
上傳時間： 2013-12-19
上傳用戶：jjj0202
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.
標簽： defuzzification fuzzification accommodates arithmetic
上傳時間： 2013-12-30
上傳用戶：dongbaobao
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).
標簽： Matlabtoolkit facilitate sequential functions
上傳時間： 2015-08-31
上傳用戶：皇族傳媒
gibbs，beyesian network，intelligent inference, Markov, BeliefPropagation. It is a very good surce code for intelligent reasoning research
標簽： gibbs
上傳時間： 2014-01-15
上傳用戶：372825274
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.
標簽： Introduction Classifiers Algorithms introduces
上傳時間： 2015-10-20
上傳用戶：aeiouetla
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.
標簽： generalized completely template describe
上傳時間： 2014-01-10
上傳用戶：hustfanenze
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
標簽： autocorrelation parametric estimates Accurate
上傳時間： 2013-12-29
上傳用戶：3到15
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.
標簽： environment computing technical Toolbox
上傳時間： 2013-12-29
上傳用戶：fandeshun
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.
標簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時間： 2020-06-10
上傳用戶：shancjb
