Adaptive statistical inferential methods for information processing

Xinjia Chen

doi:10.1117/12.2576988

8 November 2020 Adaptive statistical inferential methods for information processing

Xinjia Chen

Proceedings Volume 11525, SPIE Future Sensing Technologies; 115251U (2020) https://doi.org/10.1117/12.2576988
Event: SPIE Future Sensing Technologies, 2020, Online Only

Conference Poster

Abstract

Modern sensors produce increasingly high volume of data that requires efficient and reliable statistical methods for information processing. We consider frequent problems of information processing which can be cast into the framework of parameter estimation and multihypothesis testing. We propose a unified approach for statistical inference of information processing by introducing the inclusion principle, confidence process, unimodal likelihood estimator, and time-uniform concentration inequalities. Our methods attempt to make decision based on observing data in an adaptive and sequential way so that the decision can be made as quick as possible, while the probability of committing mistakes is acceptably small.

Conference Presentation

Citation Download Citation

Xinjia Chen "Adaptive statistical inferential methods for information processing", Proc. SPIE 11525, SPIE Future Sensing Technologies, 115251U (8 November 2020); https://doi.org/10.1117/12.2576988

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available