hidden markov models – Optimization Online

A Data-Driven Approach for a Class of Stochastic Dynamic Optimization Problems

Published: 2019/10/16, Updated: 2021/05/21

Stochastic Programming data-driven prescriptive analytics, distributionally robust optimization, hidden markov models, stochastic dynamic dual programming

Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and … Read more

A Low-Memory Approach For Best-State Estimation Of Hidden Markov Models With Model Error

Published: 2012/03/17

Mihai Anitescu

Emil M. Constantinescu

Xiaoyan Zeng

Civil and Environmental Engineering, Statistics, Unconstrained Optimization data assimilation, hidden markov models, limited-memory methods, quasi-newton methods, weakly constrained 4dvar

We present a low-memory approach for the best-state estimate (data assimilation) of hidden Markov models where model error is considered. In particular, our findings apply for the 4D- Var framework. The novelty of our approach resides in the fact that the storage needed by our estimation framework, while including model error, is dramatically reduced from … Read more