Partially observable multistage stochastic programming

Oscar Dowson*, David P. Morton, Bernardo K. Pagnoncelli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We propose a class of partially observable multistage stochastic programs and describe an algorithm for solving this class of problems. We provide a Bayesian update of a belief-state vector, extend the stochastic programming formulation to incorporate the belief state, and characterize saddle-function properties of the corresponding cost-to-go function. Our algorithm is a derivative of the stochastic dual dynamic programming method.

Original languageEnglish (US)
Pages (from-to)505-512
Number of pages8
JournalOperations Research Letters
Issue number4
StatePublished - Jul 2020


  • Bayesian
  • Multistage
  • Partially observable
  • Stochastic dual dynamic programming
  • Stochastic programming

ASJC Scopus subject areas

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics


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