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Jay I. Myung
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2020 – today
- 2020
- [c12]Sang Ho Lee, Dan Kim, John Opfer, Mark A. Pitt, Jay I. Myung:
Machine Learning Optimizes Assessment: New Insights for the Development of Numerosity Estimation. CogSci 2020 - [c11]Ran Zhou, Jay I. Myung, Mark A. Pitt:
The Scaled Target Learning Model: A Novel Computational Model of the Balloon Analogue Risk Task. CogSci 2020
2010 – 2019
- 2019
- [c10]Jay I. Myung, Mark A. Pitt, Jaeyeong Yang, Woo-Young Ahn:
Optimizing the Design of an Experiment using the ADOpy Package: An Introduction and Tutorial. CogSci 2019: 19-20 - [c9]Sang Ho Lee, Dan Kim, John Opfer, Mark A. Pitt, Jay I. Myung:
Active Learning for a Number-Line Task with Two Design Variables. CogSci 2019: 638 - [c8]Jorge Chang, Jiseob Kim, Byoung-Tak Zhang, Mark A. Pitt, Jay I. Myung:
Modeling Delay Discounting using Gaussian Process with Active Learning. CogSci 2019: 1479-1485 - 2018
- [c7]Ran Zhou, Jay I. Myung, Carol Mathews, Mark A. Pitt:
Assessing the Validity of Three Tasks of Risk-Taking Propensity: Behavioral Measure and Computational Modeling. CogSci 2018 - 2017
- [j5]Woojae Kim, Mark A. Pitt, Zhong-Lin Lu
, Jay I. Myung:
Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach. Cogn. Sci. 41(8): 2234-2252 (2017) - 2015
- [c6]Jay I. Myung, Mark A. Pitt, Maarten Speekenbrink:
Workshop on Optimizing Experimental Designs: Theory, Practice, and Applications. CogSci 2015 - 2014
- [j4]Woojae Kim, Mark A. Pitt, Zhong-Lin Lu
, Mark Steyvers
, Jay I. Myung:
A Hierarchical Adaptive Approach to Optimal Experimental Design. Neural Comput. 26(11): 2465-2492 (2014) - [c5]Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Mark Steyvers, Hairong Gu, Jay I. Myung:
A Hierarchical Adaptive Approach to the Optimal Design of Experiments. CogSci 2014 - 2013
- [j3]Daniel R. Cavagnaro
, Richard Gonzalez
, Jay I. Myung, Mark A. Pitt:
Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach. Manag. Sci. 59(2): 358-375 (2013) - [c4]Hairong Gu, Jay I. Myung, Mark A. Pitt, Zhong-Lin Lu:
Adaptive Estimation of Psychometric Slope and Threshold with Differential Evolution. CogSci 2013 - 2011
- [c3]Jay I. Myung, Mark A. Pitt:
Tutorial on Model Comparison Methods. CogSci 2011 - 2010
- [j2]Daniel R. Cavagnaro
, Jay I. Myung, Mark A. Pitt, Janne V. Kujala:
Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science. Neural Comput. 22(4): 887-905 (2010)
2000 – 2009
- 2009
- [c2]Daniel R. Cavagnaro, Mark A. Pitt, Jay I. Myung:
Adaptive Design Optimization in Experiments with People. NIPS 2009: 234-242 - 2008
- [j1]Mark A. Pitt, Jay I. Myung, Maximiliano Montenegro
, James Pooley:
Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example. Cogn. Sci. 32(8): 1285-1303 (2008) - 2005
- [c1]Jay I. Myung, Mark A. Pitt, Daniel J. Navarro:
Model selection in cognitive science as an inverse problem. Computational Imaging 2005: 219-228 - 2004
- [p1]Jay I. Myung, Mark A. Pitt:
Model Comparison Methods. Numerical Computer Methods, Part D 2004: 351-366
Coauthor Index
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