Orme summarizes a research effort and article by Kevin Van Horn, of Bayesium Analytics, where Van Horn compared various methods for implementing utility constraints for HB-MNL estimation in CBC ... Read More
Recently, Kevin van Horn (of Bayesium Analytics) did some R&D work for Sawtooth Software to compare Hamiltonian Monte Carlo (HMC) as implemented in Stan to the old standby, Sawtooth Software’s ... Read More
Finding useful covariates (variables outside the conjoint task) that can boost the predictive validity of conjoint analysis via HB estimation is a topic that Peter and Stefan have investigated and ... Read More
Sawtooth Software's Bryan Orme and Walter Williams report results of a meta analysis of about 50 commercial CBC and MaxDiff data sets. Specifically, they looked into how the priors settings in CBC/HB ... Read More
This paper was originally created for the 2009 Sawtooth Software Conference. This paper focuses on what happens during the estimation of CBC/HB utilities. It takes a naïve approach assuming no ... Read More
The basic (generic) hierarchical Bayes estimation that the first versions of Sawtooth Software's CBC/HB program supported assumed that respondents were drawn from a single, multivariate-normal ... Read More
Greg Allenby and Peter Rossi describe the history of HB methods as they relate to marketing research methods. They describe the theory behind HB, the challenges in implementing HB methods for ... Read More
Hierarchical Bayes estimation for choice data represents one of the most successful new developments in our field. HB has proven robust for ratings-based conjoint, ACA, and full-profile CBC projects. ... Read More
This paper was originally published in the March 2000 Quirk's Marketing Research Review. HB has been receiving a lot of attention lately. Until recently, desktop PCs weren't powerful enough to handle ... Read More
Conjoint analysts often discover that some of the part worths don't conform to expectations. We generally expect low prices to be preferred to high prices, high performance to low performance, etc. ... Read More
In this paper, Rich Johnson provides an intuitive example to explain Bayesian analysis. He explains how Bayesian analysis differs from conventional statistics. Rich introduces Bayes' rule, and talks ... Read More
This paper was originally delivered at the 1999 Hierarchical Bayes Conference at Ohio State University by Rich Johnson. Rich recaps his experience with HB methods, particularly as they relate to ... Read More
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