• Bayesian workflow. (Presented at University of Washington, 2017)

• Should the problems with polls make us worry about the quality of health surveys?. (Presented at Centers for Disease Control and Prevention, 2017)

• Taking Bayesian inference seriously. (Presented at Harvard conference on Big Data, 2016)

• Crimes against data. (Presented at the ESRC Research Methods Festival, Bath, England, 2016)

• Toward routine use of informative priors. (Presented at the International Conference on Machine Learning, New York, 2016)

• The statistical crisis in science. (Chief Economists' workshop, Bank of England, London, 2016)

• Bayes en médicine : Les possibilités et les risques. (Presented at the Conférence Francophone d'Epidémiologie Cliniques, Strasbourg, 2016)

• Changing everything at once: Student-centered learning, computerized practice exercises, evaluation of student progress, and a modern syllabus to create a completely new introductory statistics course. (Presented at the Electronic Conference on Teaching Statistics, 2016)

• The crisis in science and the crisis in science journalism. (Presented at Swiss Association for Science Journalism, Bern, 2016)

• Preferences in political mapping (measuring, modeling, and visualization). (Presented at conference on mapping political preferences, Toulouse, 2016)

• More than just a game: What quantitative study of sports can teach us about general principles of statistics. (Presented at Columbia University, 2016)

• What is Bayesian data analysis? Some examples. (Presented at New School, 2016)

• Learning about networks using sampling. (Presented at Washington Statistical Society, 2015)

• Easier said than done: Open problems in multilevel regression and poststratification. (Presented at conference in honor of Rod Little, University of Michigan, 2015)

• The political impact of social penumbras. (Presented at Columbia University, 2015)

• Hierarchical expectation propagation for Bayesian aggregation of average data. (Presented at Novartis Biostatistics Conference, 2015)

• Little Data: How Traditional Statistical Ideas Remain Relevant in a Big-Data World; or, The Statistical Crisis in Science; or, Open Problems in Bayesian Data Analysis. (Presented at Massachusetts Institute of Technology, 2015)

• But when you call me Bayesian I know I'm not the only one. (Presented at New York R conference, 2015)

• Recent challenges and developments in Bayesian modeling and computation (from a political and social science perspective). (Presented at Massachusetts Institute of Technology, 2015)

• "Unbiasedness": You keep using that word. I do not think it means what you think it means. (Presented at Princeton University, 2015)

• The statistical crisis in science (Presented at Columbia University, 2014, Harvard University, 2015, and the International Neuropsychological Society, 2016).
  Version presented at the German Society of Psychology methodology meeting, 2015

• Generalizing from sample to population (Presented at the University of Michigan, 2014, and Mathematica Policy Research, 2016)

• Anti-abortion Democrats, Jimmy Carter Republicans, and the missing leap day babies: Living with uncertainty but still learning (Presented at Simons Foundation, New York, 2014)
  Version presented at the Amazon Research Scientist Summit, 2015

• De la beauté, du sexe et du pouvoir : Les difficultés de l'estimation des petit effets (Presented at l'Institut du Cerveau et de la Moelle epiniere, Paris, 2014)

• Les coalitions, le pouvoir des electeurs, et l'instabilité politique (Presented at Université Paris Dauphine, 2014)
  Handout that goes with the talk

• Ethics and statistics (Presented at University of Wisconsin, 2014)

• Stan: a platform for Bayesian data analysis with complex models (Presented at German Probability and Statistics Days, Ulm, 2014. Earlier versions presented at Department of Energy Applied Mathematics Meeting, Reston, Virginia, 2011, Computer Science and Artificial Intelligence Laboratory, MIT, 2012, and University of Pennslyvania, 2014.)

• Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don't hold up? (Presented at Ecole Polytechnique, 2014, University of Amsterdam, 2013, and MCMSki, Chamonix, 2014)
  Version francaise

• Modelisation hierarchique, pooling partiel et l'interrogation des requetes virtuelles (Presented at Paris Machine Learning Meetup, 2013)

• Ronald Reagan was a statistician and other examples of learning from diverse sources of information (Presented at Montana State University, 2013)
  Version francaise

• Choices in statistical graphics: my stories (Presented at New York Data Visualization Meetup and Chicago Data Science Meetup, 2013, and Van Dantzig Seminar, Amsterdam, 2014)

• Half-life of a social statistician (Presented at Harvard University, 2012)

• Causality and statistical learning (Presented at Annual Health Economics Workshop, New York, 2012, Montana State University, 2013, University of Michigan, 2013, and University of Bristol, 2014)

• Little Data: How traditional statistical ideas remain relevant in a big-data world (Presented at Columbia University, 2012, Johns Hopkins University, 2013, and Imperial College, London, 2014)

• Weakly informative priors (Presented at Harvard University, 2011)
  Updated version (Presented at AISTATS meeting, Reykjavik, 2014)

• Mathematics, statistics, and political science (Presented at Joint Statistical Meetings, Miami, 2011)

• Hierarchical modeling and prior information: an example from toxicology. (Presented at a conference on Bayesian climate reconstruction, Lamont-Doherty Earth Observatory, 2011)

• Tradeoffs in information graphics. (Presented at MIT, 2012. Earlier versions presented at University of Kentucky, Iowa State University, and University of Michigan, 2010)

• Of beauty, sex, and power: Statistical challenges in estimating small effects. (Presented at Columbia University, 2013. Earlier versions presented at Ziff Brothers Investments, Columbia University, and Princeton University, 2010)

• Creating structured and flexible models: some open problems (Presented at Warwick University, Cambridge University, and NYC R Meetup, 2010. Earlier versions presented at Massachusetts Institute of Technology, 2008, University of California, Irvine, University of California, Berkeley, and University of British Columbia, 2009)

• Culture wars, voting, and polarization: divisions and unities in modern American politics (Presented at Columbia University and at Harvard/Manchester workshop on inequality and social change, 2010. Earlier versions presented at Harvard University, 2009, University of Washington, 2009, and Sciences Po, Paris, 2009)

• La philosophie et l'experience de la statistique bayesienne (Presented at Paris Diderot Philmath seminar, Paris, 2010)

• La polarisation politique et comment etudier ca avec la statistique (Presented at ENSAE, Paris, 2010)

• Parameterization and Bayesian modeling (Presented at the Institut Henri Poincare, Paris, 2009, University of Chicago, 2011, and Columbia University, 2011)

• Expanded graphical models: inference, model comparison, model checking, fake-data debugging, and model understanding (Presented at AppliBugs meeting, Paris, 2009, and Joint Statistical Meetings, Miami, 2011)

• Why we (usually) don't worry about multiple comparisons (Presented at Association for Public Policy Analysis and Management conference, Washington, D.C., 2007, and London School of Economics, 2009)
  Slightly different version (Presented at a meeting on statistics and neuroscience, Columbia University, 2009)

• Some computational and modeling issues for hierarchical models (Presented at the International Agency for Research on Cancer, 2009)

• Improving the presentation of quantitative results in political science (Presented, with John Kastellec, at Columbia University, 2009)

• Social and political polarization, and some other topics in network analysis (Presented at workshop in network analysis at Harvard University, 2009)

• Bayesian generalized linear models and an appropriate default prior (Presented at useR conference, Dortmund, 2008)

• Should the Democrats move left on economic policy? (Presented at Joint Statistical Meetings, Denver, 2008)

• Teaching statistics (Presented at Association for Psychological Science meeting, Chicago, 2008)

• Red state, blue state, rich state, poor state: Why Americans vote the way they do (updated after the 2008 election) (Presented at New York Young Republican Club, California Institute of Technology, Google, University of California, Berkeley, University of British Columbia, London School of Economics, 2009, and Cambridge University, 2010)

• Some recent progress in simple statistical methods (Presented at mini-symposium on statistical consulting, Applied Statistics Center, Columbia University, 2008)

• Hierarchical modeling: a unifying framework and some open questions (Presented at 50th Anniversary Meeting of the Department of Statistics, Harvard University, 2007)

• Culture wars, voting, and polarization: divisions and unities in modern American politics (Presented at Dartmouth College, 2007)
  Handout that goes with the talk

• Arsenic and old models (Presented at SAC Capital Management, New York, 2007)

• Weakly informative priors (Presented at Workshop on Monte Carlo Methods, Harvard University, 2007)

• Rich state, poor state, red state, blue state: What's the matter with Connecticut? A demonstration of multilevel modeling (Presented at Department of Economics, George Mason University, 2005, and Institute of Statistics and Decision Sciences, Duke University, 2006)
  Updated version (Presented at American Sociological Association meeting, Montreal, 2006, and Yale University, 2007. Other versions presented at Cato Institute, New America Foundation, Princeton Club of New York, and Columbia University, 2008)

• Mathematical vs. statistical models in social science (Dresden lecture, Department of Mathematics, Swarthmore College, 2005)

• Coalitions, voting power, and political instability (Dresden lecture, Department of Mathematics, Swarthmore College, 2005, and in the Math Across Campus series, University of Washington, 2009)

• Interactions in multilevel models (Presented at Joint Statistical Meetings, Minneapolis, 2005)

• Teaching statistics: a bag of tricks (Presented at Smith College, 2005). This talk was accompanied by several demonstrations and handouts, and this slideshow by itself has parts that may be hard to follow without that supplementary material.

• Some questions (and a few answers) about multilevel models (Presented at Centers for Disease Control and Prevention, 2005)

• Learning about social and political polarization using ``How many X's do you know'' surveys (Presented at Department of Statistics, Harvard University, 2005)
  Updated version (Presented at Oxford University, 2007)

• Ubiquity of multilevel models and how to understand them better. (Presented at Institute for Social Research, University of Michigan, 2004, and Department of Political Science, Stanford University, 2005)

• Fitting and understanding multilevel (hierarchical) models. (Presented at Department of Government, Harvard University, 2004)

• Survey weighting and hierarchical regression: some successes and struggles. (Presented at Department of Statistics, Yale University, 2004)

• Polls and Presidential elections. (Presented at Industrial Engineering and Operations Research seminar, Columbia University, 2004)

• Toward an environment for Bayesian data analysis in R. (Presented at Joint Statistical Meetings, Toronto, 2004)

• Computation for Bayesian data analysis. (Presented at Joint Statistical Meetings, Toronto, 2004)

• Survey weighting and hierarchical regression. (Presented at Joint Statistical Meetings, Toronto, 2004)

• Bayesian data analysis: what it is and what it is not (Presented at Department of Computer Science, Columbia University, 2003)

• Combining information and group decision making. (Presented at Workshop on Information Aggregation in Decision Making, Silver Spring, Maryland, 2003, and University of Amsterdam, 2014)

Back to Andrew Gelman's homepage.