Surprise Maximization: A Dynamic Programming Approach
arXiv:2012.14933
Abstract
Borwein et al. (2000) solved a surprise maximization problem by applying results from convex analysis and mathematical programming. Although, their proof is elegant, it requires advanced knowledge from both areas to understand it. Here, we provide another approach to derive an optimal solution of the problem by utilizing dynamic programming.
References in corpus (6)
- Modeling the Dynamics of the COVID-19 Population in Australia: A Probabilistic Analysis
- Average-reward model-free reinforcement learning: a systematic review and literature mapping
- An analytical bound on the fleet size in vehicle routing problems: a dynamic programming approach
- LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data
- The Importance of Environmental Factors in Forecasting Australian Power Demand
- Learning to Project in Multi-Objective Binary Linear Programming