variational Bayes
Covariances, Robustness, and Variational Bayes
... ational Bayes (VB) casts posterior approximation as an optimization problem in which the objective to be minimized is the divergence, among a sub-class of tractable distributions, from the exact ...field ...
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Variational Bayes inference in high dimensional time varying parameter models
... This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive inference in time-varying parameter models. Our approach involves: i) computationally trivial Kalman ...
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Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues
... Numerical optimization is a major area of mathematical study with an enormous lit- erature. Recent summaries are given in, for example, Givens and Hoeting (2005) and Ackleh et al. (2010), with the former being geared ...
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Estimation of Quantitative Trait Locus Effects with Epistasis by Variational Bayes Algorithms
... The variational Bayes method, stemming from the mean field theory in theoretical physics, is regarded as a compromise between MAP and MCMC estimation, which can be ef fi ciently computed and produces the ...
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The variational Bayes method for inverse regression problems with an application to the palaeoclimate reconstruction
... In this paper we describe an alternative approach to implementing Bayesian inference for this inverse problem. This makes use of the variational Bayes (VB) approximation. We believe that it avoids the ...
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Robust Bayesian Filtering Using Bayesian Model Averaging and Restricted Variational Bayes
... A Bayesian filter can be made robust to outliers if the Gaussian assumption is dropped in favor of a heavy-tailed distribution. A suitable choice is the use of multivariate gener- alization of Student t-distribution ...
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A Variational Bayes Approach to Clustered Latent Preference Models for Directed Network Data
... Secondly, we will introduce the reader to the directed latent clustered preference network model, which inherits ideas from the original latent space model (Hoff et al., 2002; Hoff, 2005), the model with modifications ...
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Improving the IBM Alignment Models Using Variational Bayes
... We ran our code on ten thousand sentence pairs to determine the best value of α for the transla- tion probabilities t(f |e). For our training, we ran GIZA++ for five iterations each of Model 1, the HMM, Model 3, and ...
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A Variational Bayes Approach to Decoding in a Phase Uncertain Digital Receiver
... In this case we observe each symbol transfer pe- riod sequentially and are required to infer the marginal distributions for each transferred sym- bol and phase parameter, after each symbol pe- riod. Again prior ...
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Averaged Collapsed Variational Bayes Inference
... collapsed variational Bayes (CVB) solutions have been intensively studied, especially for topic models such as latent Dirichlet allocation (LDA) (Teh et ...of variational expectation (Teh et ...
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Capsule Routing via Variational Bayes
... for its pose matrix M j , which forms a tight cluster in R D . Motivation & Contributions In this paper, we propose a new capsule routing algorithm derived from Variational Bayes. We show that our ...
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Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
... Our method is based on first selecting the functional form of the approximation. For parts of the model that are conjugate-exponential, the corresponding factorised exponential family distribution is often a good choice, ...
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Survey on Naive Bayes Algorithm
... 2. Spam filtration: It is an example of text classification. This has become a popular mechanism to distinguish spam email from legitimate email. Several modern email services implement Bayesian spam filtering. Many ...
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The Bias in Bayes and How to Measure it
... corresponding Bayes posterior intervals and showed that they are equivalent to the second order, provided the Bayes approach uses the Jeffreys (1946) root-information ...
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Bayes' theorem and quantum retrodiction
... use Bayes' theorem [8] to show how the retrodictive premeasurement density operator is simply related to the associated measurement POM element and how the (more usual) predictive density operator is similarly ...
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Bayes and health care research
... At the heart of Bayesianism is Bayes’ rule (or theorem). This is a simple and uncontroversial element of probability theory. It is based on the idea that many unknown quantities have a probability distribution. ...
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Comparing the Bayes and Typicalness Frameworks
... to Bayes when the prior is known to be correct. Unlike Bayes however, the method still gives accurate confidence values even when different data distributions are ...
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Investigative advising: a job for Bayes
... Findings: Bayes ’ theorem incorporates the isolated likelihood as one element of a three-part equation, the other parts being 1) what was known generally about the variables in the case prior to the case occurring ...
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Empirical Bayes with a changing prior
... optimal procedures are then established for both the two action problem under linear loss and the squared error loss estimation problem... Rates of convergence to optimality for..[r] ...
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Integrating Naïve Bayes and FOIL
... It would also be interesting to compare against the MACCENT system (Dehaspe, 1997), as maxi- mum entropy models and na¨ıve Bayes are somewhat related. Unfortunately, only an implementation of a propositional ...
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