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Navigating the Local Modes of Big Data: The Case of Topic Models∗ Margaret E. Roberts, Brandon M. Stewart, and Dustin Tingley This draft: June 28, 2015 ∗

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• Probability and statistics
• Statistics
• Machine learning
• Statistical natural language processing
• Bayesian statistics
• Maximum likelihood estimation
• Scientific modeling
• Topic model
• Mixture model
• Latent Dirichlet allocation
• Expectationmaximization algorithm
• Linear regression

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• Cryptocurrencies
• Blockchains
• Computing
• Alternative currencies
• Concurrent computing
• Distributed computing
• Bitcoin
• Ethereum
• Smart contract
• Rockpaperscissors
• Alice and Bob
• ternity

Structural Topic Models for Open-Ended Survey Responses Margaret E. Roberts University of California, San Diego Brandon M. Stewart Harvard University Dustin Tingley Harvard University Christopher Lucas Harvard University

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• Statistical natural language processing
• Machine learning
• Artificial intelligence
• Learning
• Topic model
• Causal inference
• Latent Dirichlet allocation
• Experiment
• Mixture model
• Unsupervised learning

Adjusting for Confounding with Text Matching∗ Margaret E. Roberts†, Brandon M. Stewart‡, and Richard A. Nielsen§ February 27, 2018¶ Abstract We identify situations in which conditioning on text can address confou

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• Design of experiments
• Causal inference
• Observational study
• Metaphysics
• Statistics
• Estimation theory
• Probability and statistics
• Regression analysis
• Propensity score matching
• Matching
• Confounding
• Average treatment effect

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Reflections About Quantitative Acid­Base This helpfile gives an introduction into the usage of the spreadsheet  Schroeck_Peter_Stewart_standard_including_P.xls (development state: 03­Aug­2009, Helmut

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