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1. Bayesian Statistics a Review
Abstract
1. Introduction. The mathematical model that has been found convenient for most statistical problems contains a sample space X of elements x endowed with an appropriate σ-field of sets over which is given a family of probability measures. These measures are indexed by a quantity, θ, called a parameter, belonging to the parameter space ϴ. The values x are referred to variously as the sample, observations or data. Notice that θ is merely an index for the various probabilities and that as a result this model includes nonparametric statistics, the special techniques in that field being necessitated by the complexity of the space ϴ which, for example, may include all distributions on the real line.
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Cited by
A practical solution to the pervasive problems ofp values
Wagenmakers, E.-J. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03194105 · Cited by 2,490
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Self-citationUsing Bayes to get the most out of non-significant results
Dienes, Z. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00781 · Cited by 1,998
(2013) · Cited by 1,351
Bayesian Artificial Intelligence
Korb, K. B., & Nicholson, A. E. (2003) · Chapman & Hall/CRC eBooks · doi:10.1201/9780203491294 · Cited by 1,014
Metadata
- Authors
- Lindley, D. V.
- Venue
- Bayesian Statistics
- Published
- 1972-01
- Pages
- 1-74
- OpenAlex
- W2069271851
- Cited by
- 521
- References
- 0
- FWCI
- 40.79
- Type
- book chapter
- Language
- en
- Open access
- closed
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