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Maximizing Mutual Information for Optimal Sensor Parameter Selection in Observation Models

This article explores the concepts of information theory, focusing on entropy, conditional entropy, and mutual information. It highlights the importance of maximum mutual information (MMI) in optimizing sensor parameter selection, particularly in the context of the 12 Coin Problem. The need for effective learning and problem-solving within observation models is emphasized, alongside techniques such as discretization and Monte Carlo methods to estimate MI. It also discusses the challenges posed by local maxima in maximizing mutual information, supported by experimental results.

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Maximizing Mutual Information for Optimal Sensor Parameter Selection in Observation Models

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Presentation Transcript

  1. Information Theory • Entropy: • Conditional Entropy: • Mutual Information:

  2. Optimal Sensor Parameter Selection • MMI: Maximum Mutual Information

  3. Example: 12 Coin Problem

  4. Problem • Need to learn: • Need to solve:

  5. Observation Model • Can be learnt over many experiments • Or, modelled by recognition system

  6. Solve argmax problem • Integral difficult to compute: • Discretise • Or, use Monte Carlo methods to estimate • Even if we can compute the MI, we also need to maximise. • Local maxima possible

  7. Experimental Results MI Max MI

  8. Experimental Results

  9. Experimental Results

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