SOWA OPAC : Katalog komputerowy książek, czasopism i zbiorów specjalnych

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Książka

W koszyku

Autor

Gonzales Ruben (1985- ). Qi Fei (1983- ). Huang Biao (1962- ).

Forma i typ

Książki Publikacje naukowe

Temat

Rachunek prawdopodobienstwa Statystyka Bayesa

Dziedzina i ujęcie

Matematyka

Bibliografia przy rozdziale. Indeks.

Motivational Illustrations Previous Work Diagnosis Techniques Monitoring Techniques Book Outline Problem Overview and Illustrative Example Overview of Proposed Work Prerequisite Fundamentals Bayesian Inference and Parameter Estimation Tutorial on Bayesian Inference Tutorial on Bayesian Inference with Time Dependency Bayesian Inference vs. Direct Inference Tutorial on Bayesian Parameter Estimation The EM Algorithm Techniques for Ambiguous Modes Tutorial on Θ Parameters in the Presence of Ambiguous Modes Tutorial on Probabilities Using Θ Parameters Dempster-Shafer Theory Kernel Density Estimation From Histograms to Kernel Density Estimates Bandwidth Selection Kernel Density Estimation Tutorial Bootstrapping Bootstrapping Tutorial Smoothed Bootstrapping Tutorial Bayesian Diagnosis Bayesian Approach for Control Loop Diagnosis Mode M Evidence E Historical Dataset D Likelihood Estimation Accounting for Autodependent Modes and Evidence Temporally Dependent Evidence Evidence Dependence Estimation of Evidence-transition Probability Issues in Estimating Dependence in Evidence Temporally Dependent Modes Mode Dependence Estimating Mode Transition Probabilities Dependent Modes and Evidence Accounting for Incomplete Discrete Evidence The Incomplete Evidence Problem Diagnosis with Incomplete Evidence Single Missing Pattern Problem Multiple Missing Pattern Problem Limitations of the Single and Multiple Missing Pattern Solutions Accounting for Ambiguous Modes: A Bayesian Approach Parametrization of Likelihood Given Ambiguous Modes Interpretation of Proportion Parameters Parametrizing Likelihoods Informed Estimates of Likelihoods Fagin-Halpern Combination Second-order Approximation Consistency of Θ Parameters Obtaining a Second-order Approximation The Second-order Bayesian Combination Rule Brief Comparison of Combination Methods Applying the Second-order Rule Dynamically Unambiguous Dynamic Solution The Second-order Dynamic Solution Making a Diagnosis Simple Diagnosis Ranged Diagnosis Expected Value Diagnosis Accounting for Ambiguous Modes: A Dempster-Shafer Approach Dempster-Shafer Theory Basic Belief Assignments Probability Boundaries Dempster’s Rule of Combination Short-cut Combination for Unambiguous Priors Generalizing Dempster-Shafer Theory Motivation: Difficulties with BBAs Generalizing the BBA Generalizing Dempster’s Rule Short-cut Combination for Unambiguous Priors Making Use of Continuous Evidence Through Kernel Density Estimation Performance: Continuous vs. Discrete Methods Average False Negative Diagnosis Criterion Performance of Discrete and Continuous Methods Kernel Density Estimation From Histograms to Kernel Density Estimates Defining a Kernel Density Estimate Bandwidth Selection Criterion Bandwidth Selection Techniques Dimension Reduction Independence Assumptions Principal and Independent Component Analysis Missing Values Kernel Density Regression Applying Kernel Density Regression for a Solution 141Dynamic Evidence Accounting for Sparse Data Within a Mode Analytical Estimation of the Monitor Output Distribution Function Control Performance Monitor Process Model Monitor Sensor Bias Monitor Bootstrap Approach to Estimating Monitor Output Distribution Function Valve Stiction Identification The Bootstrap Method Illustrative Example Applications Experimental Example 164Process Description Diagnostic Settings and Results Accounting for Sparse Modes Within the Data Approaches and Algorithms Approach for Component Diagnosis Approach for Bootstrapping New Modes Component-based Diagnosis Bootstrapping for Additional Modes Monitor Selection Component Diagnosis Introduction to Testbed Systems Simulated System Monitor Design 203Bench-scale System Industrial Scale System Bayesian Diagnosis with Discrete Data Algorithm Tutorial Simulated Case Bench-scale Case Industrial-scale Case Accounting for Autodependent Modes and Evidence Algorithms Evidence Transition Probability Mode Transition Probability Tutorial Accounting for Incomplete Discrete Evidence Single Missing Pattern Problem Multiple Missing Pattern Problem Tutorial Simulated Case Bench-scale Case Industrial-scale Case Accounting for Ambiguous Modes in Historical Data: A Bayesian Approach Algorithm Formulating the Problem Second-order Taylor Series Approximation of p(E|M,Θ) Second-order Bayesian Combination Optional Step: Separating Monitors into Independent Groups Grouping Methodology Illustrative Example of Proposed Methodology Offline Step 1: Historical Data Collection Mutual Information Criterion (Optional) Calculate Reference Values Calculate Support Calculate Second-order Terms Perform Combinations Make a Diagnosis Simulated Case Bench-scale Case Industrial-scale Case Accounting for Ambiguous Modes in Historical Data: A Dempster-Shafer Approach Parametrized Likelihoods Basic Belief Assignments The Generalized Dempster’s Rule of Combination Historical Data Collection Mutual Information Criterion (Optional) Calculate Reference Value Calculate Support Calculate the GBBA Combine BBAs and Diagnose Simulated Case Bench-scale Case Industrial System Making use of Continuous Evidence through Kernel Density Estimation Kernel Density Estimation Bandwidth Selection Adaptive Bandwidths Optional Step: Dimension Reduction by Multiplying Independent Likelihoods Creating Independence via Independent Component Analysis Replacing Missing Values Example of Proposed Methodology Historical Data Collection Mutual Information Criterion (Optional) Independent Component Analysis (Optional) Obtain Bandwidths Calculate Likelihood of New Data Calculate Posterior Probability Make a Diagnosis Simulated Case Bench-scale Case Industrial-scale Case A Code for Kernel Density Regression Kernel Density Regression Three-dimensional Matrix Toolbox Dynamic Application of Continuous Evidence and Ambiguous Mode Solutions Algorithm for Autodependent Modes Transition Probability Matrix Review of Second-order Method Second-order Probability Transition Rule Algorithm for Dynamic Continuous Evidence and Autodependent Modes Algorithm for Dynamic Continuous Evidence Combining both Solutions Comments on Usefulness Example of Proposed Methodology Offline Step 1: Historical Data Collection Create Temporal Data Mutual Information Criterion (Optional, but Recommended) Calculate Reference Values Obtain Prior Second-order Terms Calculate Support Calculate Second-order Terms Combining Prior and Likelihood Terms Simulated Case Bench-scale Case Industrial-scale Case.

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