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Bayesian emulation for multi-step optimization in decision problems

Publication ,  Journal Article
Irie, K; West, M
Published in: Bayesian Analysis
January 1, 2019

We develop a Bayesian approach to computational solution of multistep optimization problems, highlighted in the example of financial portfolio decisions. The approach involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portfolio analysis using classes of economically and psychologically relevant multi-step ahead portfolio utility functions. Studies with multivariate currency time series illustrate the approach and show some of the practical utility and benefits of the Bayesian emulation methodology.

Duke Scholars

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Published In

Bayesian Analysis

DOI

EISSN

1931-6690

ISSN

1936-0975

Publication Date

January 1, 2019

Volume

14

Issue

1

Start / End Page

137 / 160

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics
 

Citation

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Irie, K., & West, M. (2019). Bayesian emulation for multi-step optimization in decision problems. Bayesian Analysis, 14(1), 137–160. https://doi.org/10.1214/18-BA1105
Irie, K., and M. West. “Bayesian emulation for multi-step optimization in decision problems.” Bayesian Analysis 14, no. 1 (January 1, 2019): 137–60. https://doi.org/10.1214/18-BA1105.
Irie K, West M. Bayesian emulation for multi-step optimization in decision problems. Bayesian Analysis. 2019 Jan 1;14(1):137–60.
Irie, K., and M. West. “Bayesian emulation for multi-step optimization in decision problems.” Bayesian Analysis, vol. 14, no. 1, Jan. 2019, pp. 137–60. Scopus, doi:10.1214/18-BA1105.
Irie K, West M. Bayesian emulation for multi-step optimization in decision problems. Bayesian Analysis. 2019 Jan 1;14(1):137–160.

Published In

Bayesian Analysis

DOI

EISSN

1931-6690

ISSN

1936-0975

Publication Date

January 1, 2019

Volume

14

Issue

1

Start / End Page

137 / 160

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 0104 Statistics