I. J. Myung

发表

Are higher order statistics better for maximum entropy prediction in neural networks?

William B. Levy, I. J. Myung, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

An Adaptive Approach to Human Decision Making : Learning Theory , Decision Theory , and Human Performance

I. J. Myung, J. Busemeyer, 2004 .

When a good fit can be bad

I. J. Myung, M. Pitt, M. Pitt, 2002, Trends in Cognitive Sciences.

Maximum Entropy Aggregation of Expert Predictions

I. J. Myung, S. Ramamoorti, A. Bailey, 1996 .

Toward a method of selecting among computational models of cognition.

I. J. Myung, M. Pitt, Shaobo Zhang, 2002, Psychological review.

Applying Occam’s razor in modeling cognition: A Bayesian approach

I. J. Myung, M. Pitt, M. Pitt, 1997 .

GUEST EDITORS' INTRODUCTION: Special Issue on Model Selection

I. J. Myung, M. Browne, M. Forster, 2000 .

Assessing the distinguishability of models and the informativeness of data

Jay I. Myung, I. J. Myung, M. Pitt, 2004, Cognitive Psychology.

Advances in Minimum Description Length: Theory and Applications

Mark A. Pitt, Peter Grünwald, I. J. Myung, 2005 .

Algorithmic statistics and Kolmogorov’s Structure Functions

I. J. Myung, M. Pitt, P. Vitányi, 2005 .

Toward an explanation of the power law artifact: Insights from response surface analysis

I. J. Myung, M. Pitt, Cheongtag Kim, 2000, Memory & cognition.

Model Evaluation and Selection

Jay I. Myung, I. J. Myung, Daniel R. Cavagnaro, 2004 .

Is the representation meaningful? A measurement theoretic view

I. J. Myung, 1994, Behavioral and Brain Sciences.

Flexibility versus generalizability in model selection

Woojae Kim, Mark A Pitt, I. J. Myung, 2003, Psychonomic bulletin & review.

An adaptive approach to human decision making: Learning theory, decision theory, and human performance.

Jerome R. Busemeyer, I. J. Myung, In Jae Myung, 1992 .

Maximum Likelihood Estimation

I. J. Myung, In Jae Myung, 2009 .

Tutorial on maximum likelihood estimation

I. J. Myung, In Jae Myung, 2003 .

Tutorial Tutorial on maximum likelihood estimation

I. J. Myung, In Jae Myung, 2003 .

Measurement-free tests of a general state-space model of prototype learning

Jerome R. Busemeyer, I. J. Myung, In Jae Myung, 1992 .

A New Method for Investigating Prototype Learning

Jerome R. Busemeyer, I. J. Myung, In Jae Myung, 1988 .

An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science

Mark A. Pitt, Daniel J. Navarro, Woojae Kim, 2003, NIPS.

Using MCMC to Compare Network Models in Cognitive Science

I. J. Myung, M. Pitt, D. Navarro, 2003 .

Maximum Entropy Inference and Stimulus Generalization

Roger N. Shepard, I. J. Myung, In Jae Myung, 1996 .

Cue Competition Effects: Empirical Tests of Adaptive Network Learning Models

I. J. Myung, M. McDaniel, J. Busemeyer, 1993 .

Criterion Learning in a Deferred Decision-Making Task

Jerome R. Busemeyer, I. J. Myung, In Jae Myung, 1989 .

Counting probability distributions: Differential geometry and model selection

I. J. Myung, I J Myung, V Balasubramanian, 2000, Proc. Natl. Acad. Sci. USA.

Resource allocation decision making in an uncertain environment

Jerome R. Busemeyer, I. J. Myung, In Jae Myung, 1987 .

Issues in selecting mathematical models of cognition.

Mark A. Pitt, I. J. Myung, In Jae Myung, 1998 .

The Importance of Complexity in Model Selection.

I. J. Myung, Myung, 2000, Journal of mathematical psychology.

Maximum entropy interpretation of decision bound and context models of categorization

I. J. Myung, In Jae Myung, 1994 .

ACCEPTED VERSION

I. J. Myung, J. Pitcher, J. Owens, 2003 .

ACCEPTED VERSION

I. J. Myung, Daniel J. Navarro, Mark A. Pitt, 2003 .