M. Steyvers

Publications
Influence

M. Steyvers, T. Griffiths
Computer Science
15 February 2007

The Large-Scale Structure of Semantic Networks

M. Steyvers, Josh Tenenbaum
Computer Science
2005

A method for efficiently sampling from distributions with correlated dimensions.

B. Turner, P. Sederberg, S. Brown, M. Steyvers
Medicine, Psychology
Psychological methods
6 May 2013

Bayesian estimation has played a pivotal role in the understanding of individual differences. However, for many models in psychology, Bayesian estimation of model parameters can be difficult. One… Expand

Accumulative prediction error and the selection of time series models

Eric-Jan Wagenmakers, P. Grünwald, M. Steyvers
Computer Science
1 April 2006

We explore the rationale for using accumulative one-step-ahead prediction error (APE) as a data-driven method for model selection. Expand

Computational Statistics with Matlab

M. Steyvers

Morphing techniques for manipulating face images

M. Steyvers
Computer Science, Medicine
Behavior research methods, instruments…
1 June 1999

This paper describes morphing techniques to manipulate two-dimensional human face images and three-dimensional models of the human head. Expand

Graph theoretic analyses of semantic networks: Small worlds in semantic networks

M. Steyvers, J. Tenenbaum
Computer Science
2005

Scalable Parallel Topic Models

D. Newman, Padhraic Smyth, M. Steyvers
Computer Science
2006

We present a parallel algorithm for the topic model that has linear speedup and high parallel efficiency for shared-memory symmetric multiprocessors. Expand

Joint Models of Neural and Behavioral Data

B. Turner, B. Forstmann, M. Steyvers
Computer Science
5 January 2019

This book presents a flexible Bayesian framework for combining neural and cognitive models that allows the neural data to influence the parameters of the cognitive model and allows behavioral data to constrain the neural model. Expand

Hierarchical Bayesian Analyses for Modeling BOLD Time Series Data

M. Molloy, Giwon Bahg, X. Li, M. Steyvers, Z. Lu, B. Turner
Computer Science
1 June 2018

We use the convolution technique as a basis for describing neural time series data and develop five models to describe how subject-, condition-, and brain-area-specific effects interact. Expand

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