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Hierarchical bayes conjoint choice models : model framework, Bayesian inference, model selection, and interpretation of estimation results

Goeken, Nils; Kurz, Peter; Steiner, Winfried J. - In: Marketing : ZFP ; journal of research and management 43 (2021) 3, pp. 49-66

Persistent link: https://www.econbiz.de/10012817650

Updating variational Bayes : fast sequential posterior inference

Tomasetti, Nathaniel; Forbes, Catherine Scipione; … - 2020

Persistent link: https://www.econbiz.de/10012608357

Particle learning for Bayesian semi-parametric stochastic volatility model

Virbickaitė, Audronė; Lopes, Hedibert Freitas; … - In: Econometric reviews 38 (2019) 9, pp. 1007-1023

Persistent link: https://www.econbiz.de/10012181379

An approach to improve the predictive power of choice-based conjoint analysis

Voleti, Sudhir; Srinivasan, V.; Ghosh, Pulak - In: International journal of research in marketing : IJRM ; … 34 (2017) 2, pp. 325-335

Persistent link: https://www.econbiz.de/10011734857

Updating Variational Bayes : fast sequential posterior inference

Tomasetti, Nathaniel; Forbes, Catherine Scipione; … - 2019

Persistent link: https://www.econbiz.de/10012592824

Particle learning for Bayesian non-parametric Markov Switching Stochastic Volatility model

Virbickaite, Audrone; Lopes, Hedibert F.; Ausín, Concepcion - Departamento de Estadistica, Universidad Carlos III de … - 2014

This paper designs a Particle Learning (PL) algorithm for estimation of Bayesian nonparametric Stochastic Volatility (SV) models for financial data. The performance of this particle method is then compared with the standard Markov Chain Monte Carlo (MCMC) methods for non-parametric SV models. PL...

Persistent link: https://www.econbiz.de/10010940764

Multimodal preference heterogeneity in choice-based conjoint analysis : a simulation study

Goeken, Nils; Kurz, Peter; Steiner, Winfried J. - In: Journal of business economics : JBE 94 (2024) 1, pp. 137-185

Persistent link: https://www.econbiz.de/10014494374