Journal ArticleDOI
Tutorial on maximum likelihood estimation
TLDR
The purpose of this paper is to provide a good conceptual explanation of the method with illustrative examples so the reader can have a grasp of some of the basic principles of MLE.About:
This article is published in Journal of Mathematical Psychology.The article was published on 2003-02-01. It has received 1542 citations till now. The article focuses on the topics: Maximum likelihood sequence estimation & Estimation theory.read more
Citations
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Journal ArticleDOI
Discrete fixed-resolution representations in visual working memory
Weiwei Zhang,Steven J. Luck +1 more
TL;DR: It is shown that, when presented with more than a few simple objects, human observers store a high-resolution representation of a subset of the objects and retain no information about the others.
Book
Bayesian Cognitive Modeling: A Practical Course
TL;DR: In this article, the basics of Bayesian analysis are discussed, and a WinBUGS-based approach is presented to get started with WinBUGs, which is based on the SIMPLE model of memory.
Journal ArticleDOI
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications.
Eric-Jan Wagenmakers,Maarten Marsman,Tahira Jamil,Alexander Ly,Josine Verhagen,Jonathon Love,Ravi Selker,Quentin Frederik Gronau,Martin Šmíra,Sacha Epskamp,Dora Matzke,Jeffrey N. Rouder,Richard D. Morey +12 more
TL;DR: Ten prominent advantages of the Bayesian approach are outlined, and several objections to Bayesian hypothesis testing are countered.
MonographDOI
Social Media Mining: An Introduction
TL;DR: Social Media Mining introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.
Journal ArticleDOI
On the ability to inhibit thought and action: general and special theories of an act of control.
TL;DR: A general race model is presented that extends the independent race model to account for the role of choice in go and stop processes, and a special race model that assumes each runner is a stochastic accumulator governed by a diffusion process is applied.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Proceedings Article
Information Theory and an Extention of the Maximum Likelihood Principle
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Book ChapterDOI
Information Theory and an Extension of the Maximum Likelihood Principle
TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.