# Sequential Controlled Sensing for Composite Multihypothesis Testing

arXiv: https://arxiv.org/abs/1910.12697 · HTML: https://mlsanity.com/paper/1910.12697/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Learning to Detect an Odd Restless Markov Arm with a Trembling Hand](https://mlsanity.com/paper/2105.03603/) (2021)
- [Best Arm Identification in Restless Markov Multi-Armed Bandits](https://mlsanity.com/paper/2203.15236/) (2022)
- [Exponentially Consistent Nonparametric Linkage-Based Clustering of Data Sequences](https://mlsanity.com/paper/2411.13922/) (2024)
- [Online Clustering of Data Sequences with Bandit Information](https://mlsanity.com/paper/2501.11421/) (2025)
- [Efficient Clustering in Stochastic Bandits](https://mlsanity.com/paper/2601.09162/) (2026)

## Abstract (arXiv)

The problem of multi-hypothesis testing with controlled sensing of observations is considered. The distribution of observations collected under each control is assumed to follow a single-parameter exponential family distribution. The goal is to design a policy to find the true hypothesis with minimum expected delay while ensuring that the probability of error is below a given constraint. The decision-maker can control the delay by intelligently choosing the control for observation collection in each time slot. We derive a policy that satisfies the given constraint on the error probability. We also show that the policy is asymptotically optimal in the sense that it asymptotically achieves an information-theoretic lower bound on the expected delay.