Stephen Winters-Hilt

Informatics and Machine Learning (eBook, PDF)

From Martingales to Metaheuristics

• Format: PDF

Produktbeschreibung

Informatics and Machine Learning Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how analyze any data captured in digital form. The book describes how readers can conduct analyses of text, general sequential data, experimental observations over time, stock market and econometric histories, or symbolic data, like genomes. It contains large amounts of sample code to demonstrate the concepts contained within and assist with various levels of project work. The book offers a complete presentation of the mathematical underpinnings of a wide variety of forms of data analysis and provides extensive examples of programming implementations. It is based on two decades worth of the distinguished author's teaching and industry experience. * A thorough introduction to probabilistic reasoning and bioinformatics, including Python shell scripting to obtain data counts, frequencies, probabilities, and anomalous statistics, or use with Bayes' rule * An exploration of information entropy and statistical measures, including Shannon entropy, relative entropy, maximum entropy (maxent), and mutual information * A practical discussion of ad hoc, ab initio, and bootstrap signal acquisition methods, with examples from genome analytics and signal analytics Perfect for undergraduate and graduate students in machine learning and data analytics programs, Informatics and Machine Learning: From Martingales to Metaheuristics will also earn a place in the libraries of mathematicians, engineers, computer scientists, and life scientists with an interest in those subjects.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 592
• Erscheinungstermin: 16. Dezember 2021
• Englisch
• ISBN-13: 9781119716570
• Artikelnr.: 63207336

Autorenporträt

Stephen Winters-Hilt, PhD, is Sole Proprietor at Meta Logos Systems, Albuquerque, NM, USA, which specializes in Machine Learning, Signal Analysis, Financial Analytics, and Bioinformatics. He received his doctorate in Theoretical Physics from the University of Wisconsin, as well as a PhD in Computer Science and Bioinformatics from the University of California, Santa Cruz.

Inhaltsangabe

Preface xv

1 Introduction 1

1.1 Data Science: Statistics, Probability, Calculus ... Python (or Perl) and Linux 2

1.2 Informatics and Data Analytics 3

1.3 FSA-Based Signal Acquisition and Bioinformatics 4

1.4 Feature Extraction and Language Analytics 7

1.5 Feature Extraction and Gene Structure Identification 8

1.5.1 HMMs for Analysis of Information Encoding Molecules 11

1.5.2 HMMs for Cheminformatics and Generic Signal Analysis 11

1.6 Theoretical Foundations for Learning 13

1.7 Classification and Clustering 13

1.8 Search 14

1.9 Stochastic Sequential Analysis (SSA) Protocol (Deep Learning Without NNs) 15

1.9.1 Stochastic Carrier Wave (SCW) Analysis - Nanoscope Signal Analysis 18

1.9.2 Nanoscope Cheminformatics - A Case Study for Device "Smartening" 19

1.10 Deep Learning using Neural Nets 20

1.11 Mathematical Specifics and Computational Implementations 21

2 Probabilistic Reasoning and Bioinformatics 23

2.1 Python Shell Scripting 23

2.1.1 Sample Size Complications 33

2.2 Counting, the Enumeration Problem, and Statistics 34

2.3 From Counts to Frequencies to Probabilities 35

2.4 Identifying Emergent/Convergent Statistics and Anomalous Statistics 35

2.5 Statistics, Conditional Probability, and Bayes' Rule 37

2.5.1 The Calculus of Conditional Probabilities: The Cox Derivation 37

2.5.2 Bayes' Rule 38

2.5.3 Estimation Based on Maximal Conditional Probabilities 38

2.6 Emergent Distributions and Series 39

2.6.1 The Law of Large Numbers (LLN) 39

2.6.2 Distributions 39

2.6.3 Series 42

2.7 Exercises 42

3 Information Entropy and Statistical Measures 47

3.1 Shannon Entropy, Relative Entropy, Maxent, Mutual Information 48

3.1.1 The Khinchin Derivation 49

3.1.2 Maximum Entropy Principle 49

3.1.3 Relative Entropy and Its Uniqueness 51

3.1.4 Mutual Information 51

3.1.5 Information Measures Recap 52

3.2 Codon Discovery from Mutual Information Anomaly 58

3.3 ORF Discovery from Long-Tail Distribution Anomaly 66

3.3.1 Ab initio Learning with smORF's, Holistic Modeling, and Bootstrap Learning 69

3.4 Sequential Processes and Markov Models 72

3.4.1 Markov Chains 73

3.5 Exercises 75

4 Ad Hoc, Ab Initio, and Bootstrap Signal Acquisition Methods 77

4.1 Signal Acquisition, or Scanning, at Linear Order Time-Complexity 77

4.2 Genome Analytics: The Gene-Finder 80

4.3 Objective Performance Evaluation: Sensitivity and Specificity 93

4.4 Signal Analytics: The Time-Domain Finite State Automaton (tFSA) 93

4.4.1 tFSA Spike Detector 95

4.4.2 tFSA-Based Channel Signal Acquisition Methods with Stable Baseline 98

4.4.3 tFSA-Based Channel Signal Acquisition Methods Without Stable Baseline 103

4.5 Signal Statistics (Fast): Mean, Variance, and Boxcar Filter 107

4.5.1 Efficient Implementations for Statistical Tools (O(L)) 109

4.6 Signal Spectrum: Nyquist Criterion, Gabor Limit, Power Spectrum 110

4.6.1 Nyquist Sampling Theorem 110

4.6.2 Fourier Transforms, and Other Classic Transforms 110

4.6.3 Power Spectral Density 111

4.6.4 Power-Spectrum-Based Feature Extraction 111

4.6.5 Cross-Power Spectral Density 112

4.6.6 AM/FM/PM Communications Protocol 112

4.7 Exercises 112

5 Text Analytics 125

5.1 Words 125

5.1.1 Text Acquisition: Text Scraping and As