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Property Testing - A Learning Theory Perspective - Bok av Dana Ron - Häftad

Takes the learning-theory point of view of property testing and focuses on results for testing properties of functions that are of interest to the learning theory community. In particular the book covers results for testing algebraic properties of functions such as linearity.

SEK 1120.00

1

Probabilistic Trace and Testing Semantics - Bok av Marco Bernardo - Häftad

SEK 1116.00

1

Topics and Techniques in Distribution Testing - Bok av Clément L. Canonne - Häftad

SEK 1440.00

1

Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle - Bok av Tom Engsted - Häftad

Non-Experimental Data, Hypothesis Testing, and the Likelihood Principle: A Social Science Perspective argues that frequentist hypothesis testing - the dominant statistical evaluation paradigm in empirical research - is fundamentally unsuited for analysis of the non-experimental data prevalent in economics and other social sciences. Frequentist tests comprise incompatible repeated sampling frameworks that do not obey the Likelihood Principle (LP). For probabilistic inference, methods that are guided..

SEK 840.00

1

Group Testing - An Information Theory Perspective - Bok av Matthew Aldridge - Häftad

The focus of this book is on the non-adaptive setting of group testing. In this setting, the test pools are designed in advance enabling them to be implemented in parallel. The book gives a comprehensive and thorough treatment of the subject from an information theoretic perspective, and covers several related developments.

SEK 1466.00

1

Information Theory and Statistics - A Tutorial - Bok av Imre Csiszar - Häftad

Explores the applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an \information geometry\ background.

SEK 507.00

1

Recent Advances in Testing Techniques for AI Hardware Accelerators - Bok av Arjun Chaudhuri - Häftad

The rapid growth in big data from mobile, Internet of things (IoT), and edge devices, and the continued demand for higher computing power, have established deep learning as the cornerstone of most artificial intelligence (AI) applications today. Recent years have seen a push towards deep learning implemented on domain-specific AI accelerators that support custom memory hierarchies, variable precision, and optimized matrix multiplication. Commercial AI accelerators have shown superior energy and footprint..

SEK 1227.00

1

Operating Characteristics for Classical and Quantum Binary Hypothesis Testing - Bok av Catherine A. Medlock - Häftad

Written in a tutorial style, readers from both classical and quantum backgrounds will find this an enlightening treatise on the topic. This monograph is a comprehensive and accessible overview of a complex problem for students and researchers in signal processing.

SEK 1341.00

1

A Complete Framework for Model-Free Difference-in-Differences Estimation - Bok av Daniel J. Henderson - Häftad

A Complete Framework for Model-Free Difference-in-Differences Estimation proposes a complete framework for data-driven difference-in-differences analysis with covariates, in particular nonparametric estimation and testing. The authors start with simultaneously choosing confounders and a scale of the outcome along identification conditions. They estimate first heterogeneous treatment effects stratified along the covariates, then the average effect(s) for the treated. This provides the asymptotic and..

SEK 1066.00

1

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