Implicit and Non-parametric Shape Reconstruction from Unorganized Data using a variational Level Set Method
標(biāo)簽： Non-parametric Reconstruction Unorganized variational
上傳時(shí)間： 2016-03-02
上傳用戶：wangdean1101
計(jì)算電磁學(xué)的３維變分標(biāo)量有限元方法程序。EMAP1 employs a variational formulation。
標(biāo)簽： variational formulation employs EMAP1
上傳時(shí)間： 2013-12-13
上傳用戶：hanli8870
The library is a C++/Python implementation of the variational building block framework introduced in our papers. The framework allows easy learning of a wide variety of models using variational Bayesian learning
標(biāo)簽： implementation variational introduced framework
上傳時(shí)間： 2016-12-16
上傳用戶：eclipse
variational Bayesian Multinomial Probit Regression with Gaussian Process Priors.
標(biāo)簽： variational Multinomial Regression Bayesian
上傳時(shí)間： 2014-01-11
上傳用戶：TF2015
variational Bayes by EmtiyazKhan
標(biāo)簽： variational EmtiyazKhan Bayes by
上傳時(shí)間： 2017-03-13
上傳用戶：athjac
機(jī)器學(xué)習(xí)大牛Jordan的書籍《Graphical Models,Exponential Families,and variational Inference》
標(biāo)簽： Exponential variational Graphical Inference
上傳時(shí)間： 2013-12-09
上傳用戶：sssl
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(biāo)簽： instantaneous algorithm Bayesian Gaussian
上傳時(shí)間： 2013-12-19
上傳用戶：jjj0202
水平集代碼及文章（李春明）：Level Set Evolution Without Re-initialization: A New variational Formulation
標(biāo)簽： 水平集代碼及文章（李春明）
上傳時(shí)間： 2015-03-16
上傳用戶：FQ967
Bulletin of the American Mathematical Society Volume 49 issue 1 1943 [doi 10.1090_s0002-9904-1943-07818-4] Courant, R. -- variational methods for the solution of problems of equilibrium and vibratio
上傳時(shí)間： 2020-05-10
上傳用戶：藍(lán)天自由
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(biāo)簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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