Adaptation of Hierarchical Bayes SAE to Spatial Satscan

Hierarchical Bayes for Text Classification

Naive Bayes models have been very popular in several classification tasks. In this paper we study the application of these models to classification tasks where the data is sparse i.e., a large number of possible outcomes do not appear in the data. Traditionally point estimates of the model parameters and in particular, point estimates based on the Laplace’s rule have been popular for such spars...

Chapter 20 HIERARCHICAL BAYES MODEL

Adaptation of Topic Model to New Domains Using Recursive Bayes

In real-world applications, the topic model using latent Dirichlet allocation (LDA) should be adaptive to meet the unknown domain knowledge. Compensating such domain mismatch assures a robust document representation at different time stamps. This study presents a recursive Bayes algorithm to implement an adaptive topic model (ATM) in which LDA is evolved to new domains from adaptation data epoc...

Simultaneous Estimation of Disease Risks and Spatial Clustering: A Hierarchical Bayes Approach

Detection of clustering and estimation of incidence risks from disease data are important in public health and epidemiological research. The popular models for disease risks such as conditional autoregressive (CAR) models assume known spatial dependence structure. Instead, we consider spatial clusters in which areas are geographically connected. Given spatial clustering idea, we propose a metho...

Hierarchical Bayes and Empirical Bayes. Mlii Method. 1.1 Hierarchical Bayesian Analysis

Hierarchical Bayes and Empirical Bayes are related by their goals, but quite different by the methods of how these goals are achieved. The attribute hierarchical refers mostly to the modeling strategy, while empirical is referring to the methodology. Both methods are concerned in specifying the distribution at prior level, hierarchical via Bayes inference involving additional degrees of hierarc...