Abstract
This article discusses Bayesian nonparametric models, arguing that all Bayesians are constructing probability distributions (the prior) on spaces of density functions. The parametric Bayesian can be seen to be making restrictive assumptions about the choice of density for modelling data. In contrast, the nonparametric Bayesian constructs a probability distribution on as many densities as possible. The model is infinite dimensional, yet inference is possible, including density estimation and the implementation of decision rules, such as the maximization of expected utility. An example of a nonparametric model is given and a means by which to make inference provided by simulation techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Bernardo, J.M., and A.F.M. Smith. 1994. Bayesian theory. London: Wiley.
Escobar, M.D. 1988. Estimating the means of several normal populations by nonparametric estimation of the distribution of the means. Ph.D. thesis, Department of Statistics, Yale University.
Ferguson, T.S. 1973. A Bayesian analysis of some nonparametric problems. Annals of Statistics 1: 209–230.
Hirshleifer, J., and J.G. Riley. 1992. The analysis of uncertainty and information. Cambridge: Cambridge University Press.
Lindley, D.V. 1978. The Bayesian approach (with discussion). Scandinavian Journal of Statistics 5: 1–26.
Lindsey, J.K. 1999. Some statistical heresies. The Statistician 48: 1–40.
Lo, A.Y. 1984. On a class of Bayesian nonparametric estimates I Density estimates. Annals of Statistics 12: 351–357.
Smith, A.F.M., and G.O. Roberts. 1993. Bayesian computations via the Gibbs sampler and related Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B 55: 3–23.
Tierney, L. 1994. Markov chains for exploring posterior distributions. Annals of Statistics 22: 1701–1722.
Walker, S.G., P. Damien, P.W. Laud, and A.F.M. Smith. 1999. Bayesian nonparametric inference for random distributions and related functions (with discussion). Journal of the Royal Statistical Society, Series B 61: 485–527.
Author information
Authors and Affiliations
Editor information
Copyright information
© 2018 Macmillan Publishers Ltd.
About this entry
Cite this entry
Walker, S.G. (2018). Bayesian Non-parametrics. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2596
Download citation
DOI: https://doi.org/10.1057/978-1-349-95189-5_2596
Published:
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-95188-8
Online ISBN: 978-1-349-95189-5
eBook Packages: Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences