January 1, 1987

Hierarchical Bayesian Analysis Using Monte Carlo Integration: Computing Posterior Distributions When There are Many Possible Models

Key Points

Key points are not available for this paper at this time.

Abstract

Leland Stewart, Hierarchical Bayesian Analysis Using Monte Carlo Integration: Computing Posterior Distributions When There are Many Possible Models, Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 36, No. 2/3, Special Issue: Practical Bayesian Statistics (1987), pp. 211-219

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Discussion

Cite this study

Leland Stewart (Thu,) studied this question.

www.synapsesocial.com/papers/6a0e98f34a1f6bc4780435e9 — DOI: https://doi.org/10.2307/2348514

Authors

Leland Stewart

Journals

Journal of the Royal Statistical Society Series D (The Statistician)

Actions

Institutions

Lockheed Martin (United States)

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Hierarchical Bayesian Analysis Using Monte Carlo Integration: Computing Posterior Distributions When There are Many Possible Models

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Cite this study

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion