Sequential Experimental Design

Sequential experimental design focuses on optimizing the selection of experiments over time to maximize information gain or achieve a specific objective, such as efficient model parameter estimation or targeted causal inference. Current research emphasizes the use of Bayesian methods, often coupled with reinforcement learning algorithms (like actor-critic methods or policy gradient methods) or advanced filtering techniques (e.g., particle filters), to efficiently navigate complex, high-dimensional design spaces, even with constraints like switching costs or limited budgets. This approach is proving valuable across diverse fields, improving the efficiency of scientific investigations and enabling more effective resource allocation in applications ranging from materials science to medical imaging.