A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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This article is published in Annals of Mathematical Statistics.The article was published on 1970-02-01 and is currently open access. It has received 4618 citations till now. The article focuses on the topics: Examples of Markov chains & Markov chain.read more
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Correction to "discriminant-function-based minimum recognition error rate pattern-recognition approach to speech recognition"
TL;DR: A discriminant function-based minimum recognition error rate pattern recognition approach is described and studied for various applications in speech processing and its suitability for classification error rate minimization is established through a special loss function.
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Model-based Utility Functions
TL;DR: In this paper, the authors argue that the behavior problems of self-delusion can be avoided by formulating the utility function in two steps: inferring a model of the environment from interactions, and computing utility as a function of an environment model.
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Adaptive and Energy Efficient Context Representation Framework in Mobile Sensing
TL;DR: A novel framework that includes an inhomogeneous (time-variant) Hidden Markov Model (HMM) and learning from data concepts and to maximize power efficiency while the model operates is presented.
Proceedings ArticleDOI
Unsupervised discovery of intentional process models from event logs
TL;DR: A novel approach, so-called Map Miner Method (MMM), designed to automate the construction of intentional process models from process logs, which offers a new understanding of software processes, and could readily be used for recommender systems.
References
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Journal ArticleDOI
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Leonard E. Baum,Ted Petrie +1 more
Journal ArticleDOI
An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
Leonard E. Baum,J. A. Eagon +1 more
TL;DR: In this paper, a polynomial with nonnegative coefficients homogeneous of degree d in its variables is shown to be polynomially homogeneous unless 3(3(x))>P(x), where 3(x)=x.
Book ChapterDOI
The Gamma Function
Willi Freeden,Martin Gutting +1 more
TL;DR: The Gamma function as discussed by the authors is a generalized factorial function that can be used to estimate the probability distribution of a probability distribution, and it has been used in many applications, e.g., as part of probability distributions.