Abstract: We give a sufficient condition for admissibility of generalized Bayes estimators of the location vector of spherically symmetric distribution under squared error loss. Compared to the known results for the multivariate normal case, our sufficient condition is very tight and is close to being a necessary condition. In particular we establish the admissibility of generalized Bayes estim...

This essay investigates the question of how the naive Bayes classifier and the support vector machine compare in their ability to forecast the Stock Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier is explored. The algorithms are trained using data from the month of January 2010, extracted from the MarketWatch.com website. Input features are selected based on previ...

In this study, we build a causal map of the investment decision using causal mapping techniques by interviewing venture capitalists (VCs). We convert this map into a causal Bayes net using techniques we developed. Causal Bayes nets are especially suited for domains characterized by high degree of uncertainty. Bayes nets have been recently developed in artificial intelligence and used in medical...

While much of the school choice literature advocates strategyproofness, recent research has aimed to improve efficiency using mechanisms that rely on non-truthtelling equilibria. We address two issues that arise from this approach. We first show that even in simple environments with ample feedback and repetition, agents fail to reach non-truthtelling equilibria. We offer another way forward: im...

In this letter, a Hierarchical Parametric Empirical Bayes model is proposed to model spike count data. We have integrated Generalized Linear Models (GLMs) and empirical Bayes theory to simultaneously provide three advantages: (1) a model of over-dispersion of spike count values; (2) reduced MSE in estimation when compared to using the maximum likelihood method for GLMs; and (3) an efficient alt...

Three commonly used techniques for combining uncertain evidence are explored with reference to different three types of land cover knowledge: numerical distributions, relative spectral distances, and human expert “rules of thumb”. In attempting to combine such evidence Bayes’, Dempster-Shafer and Endorsement theories answer different questions depending on nature of the land cover evidence (com...