2013
NIPS
NeurIPS 2013
Generalized Random Utility Models with Multiple Types
Abstract
We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— random utility
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Hot Topic Early Bird
— markov chain monte carlo
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Bayesian Inference
Mathematics & Optimization > Optimization > Stochastic Methods
Machine Learning > Learning Types > Multi-Agent Systems
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Mathematics & Optimization > Optimization > Game Theory