We augment the naive Bayes model with an n-gram language model to address two shortcomings of naive Bayes text classifiers. The chain augmented naive Bayes classifiers we propose have two advantages over standard naive Bayes classifiers. First, a chain augmented naive Bayes model relaxes some of the independence assumptions of naive Bayes—allowing a local Markov chain dependence in the observed...

In the Ades et al. method we calculate, for each sampled dataset, either the exact posterior expected net benefit for each decision option, or some close analytic approximation. We denote the analytically calculated posterior expected net benefit for data sample k and decision option d as NB

an estimation problem of the mean µ of an inverse gaussian distribution ig(µ, c µ) with known coefficient of variation c is treated as a decision problem with entropy loss function. a class of bayes estimators is constructed, and shown to include mrse estimator as its closure. two important members of this class can easily be computed using continued fractions

We generalize the approach of Liu and Lawrence (1999) for multiple changepoint problems where the number of changepoints is unknown. The approach is based on dynamic programming recursion for efficient calculation of the marginal probability of the data with the hidden parameters integrated out. For the estimation of the hyperparameters, we propose to use Monte Carlo EM when training data are a...

cancer is one of the causes of death in the world. several treatment methods exist against cancer cells such as radiotherapy and chemotherapy. since traditional methods have side effects on normal cells and are expensive, identification and developing a new method to cancer therapy is very important. antimicrobial peptides, present in a wide variety of organisms, such as plants, amphibians and ...

This paper aims to study the empirical Bayes estimation of the parameter of ЭРланга distribution under a weighted squared error loss function. Bayes estimator is firstly to derive based on pivot method. Then empirical Bayes estimator of unknown parameter is constructed in a priori unknown circumstances. The asymptotically optimal property of this empirical Bayes estimator is also discussed. It ...

The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total expected square loss is a particularly interesting performance measure: (a) this quantity is finitely bounded, implying convergence with probability one, and (b) it additionally specifies the convergence speed. For MD...

Multiple testing has been widely adopted for genome-wide studies such as microarray experiments. For effective gene selection in these genome-wide studies, the optimal discovery procedure (ODP), which maximizes the number of expected true positives for each fixed number of expected false positives, was developed as a multiple testing extension of the most powerful test for a single hypothesis b...