Reliability and Risk A Bayesian Perspective 1st Edition by Nozer D Singpurwalla ISBN 0470855029 9780470855027

Reliability and Risk A Bayesian Perspective 1st Edition by Nozer D Singpurwalla – Ebook PDF Instant Download/Delivery: 0470855029, 9780470855027
Full download Reliability and Risk A Bayesian Perspective 1st Edition after payment

Product details:

ISBN 10: 0470855029 
ISBN 13: 9780470855027
Author: Nozer D Singpurwalla

We all like to know how reliable and how risky certain situations are, and our increasing reliance on technology has led to the need for more precise assessments than ever before. Such precision has resulted in efforts both to sharpen the notions of risk and reliability, and to quantify them. Quantification is required for normative decision-making, especially decisions pertaining to our safety and wellbeing. Increasingly in recent years Bayesian methods have become key to such quantifications.

Reliability and Risk provides a comprehensive overview of the mathematical and statistical aspects of risk and reliability analysis, from a Bayesian perspective. This book sets out to change the way in which we think about reliability and survival analysis by casting them in the broader context of decision-making. This is achieved by:

• Providing a broad coverage of the diverse aspects of reliability, including: multivariate failure models, dynamic reliability, event history analysis, non-parametric Bayes, competing risks, co-operative and competing systems, and signature analysis.
• Covering the essentials of Bayesian statistics and exchangeability, enabling readers who are unfamiliar with Bayesian inference to benefit from the book.
• Introducing the notion of “composite reliability”, or the collective reliability of a population of items.
• Discussing the relationship between notions of reliability and survival analysis and econometrics and financial risk.

Reliability and Risk can most profitably be used by practitioners and research workers in reliability and survivability as a source of information, reference, and open problems. It can also form the basis of a graduate level course in reliability and risk analysis for students in statistics, biostatistics, engineering (industrial, nuclear, systems), operations research, and other mathematically oriented scientists, wherein the instructor could supplement the material with examples and problems.

Reliability and Risk A Bayesian Perspective 1st Table of contents:

Part I: Foundations of Bayesian Inference

• Chapter 1: Introduction to Reliability and Risk Analysis
  • What is Reliability? Definitions and Concepts
  • What is Risk? Measures and Perceptions
  • Sources of Uncertainty in Engineering and Systems
  • Deterministic vs. Probabilistic Approaches
  • The Need for a Bayesian Framework: Incorporating Prior Knowledge
• Chapter 2: Review of Probability Theory
  • Axioms of Probability
  • Conditional Probability and Independence
  • Random Variables and Probability Distributions (Discrete and Continuous)
  • Joint, Marginal, and Conditional Distributions
  • Moments and Expectation
• Chapter 3: The Bayesian Paradigm
  • Bayes’ Theorem: The Core of Bayesian Inference
  • Prior Distributions: Informative vs. Non-Informative Priors
  • Likelihood Function: Connecting Data to Parameters
  • Posterior Distribution: Updating Beliefs with Data
  • Predictive Distributions
• Chapter 4: Basic Bayesian Inference for Common Distributions
  • Inference for Binomial Proportions (Reliability of Bernoulli Trials)
  • Inference for Poisson Rates (Reliability of Counts)
  • Inference for Normal Mean and Variance
  • Conjugate Priors and Their Role

Part II: Bayesian Reliability Analysis

• Chapter 5: Bayesian Inference for Lifetime Distributions
  • The Exponential Distribution: Prior, Likelihood, Posterior, Predictive Analysis
  • The Weibull Distribution: Parameters and Inference
  • The Lognormal Distribution
  • Other Lifetime Distributions (e.g., Gamma, Inverse Gaussian)
• Chapter 6: Analysis of Censored Data
  • Types of Censoring (Type I, Type II, Random Censoring)
  • Incorporating Censored Data into Bayesian Likelihoods
  • Case Studies with Censored Reliability Data
• Chapter 7: System Reliability from a Bayesian Perspective
  • Series, Parallel, and K-out-of-N Systems
  • Reliability Block Diagrams and Fault Trees
  • Bayesian Inference for System Reliability Parameters
  • Dependence Modeling in Systems
• Chapter 8: Accelerated Life Testing
  • Models for Accelerated Testing (Arrhenius, Inverse Power Law, Eyring)
  • Bayesian Design and Analysis of Accelerated Life Tests
  • Predicting Field Reliability from Accelerated Data
• Chapter 9: Degradation Models and Prognostics
  • Modeling Degradation Paths (e.g., Wiener Process, Gamma Process)
  • Bayesian Inference for Degradation Parameters
  • Predicting Remaining Useful Life (RUL)
  • Prognostics and Health Management (PHM)

Part III: Bayesian Risk Assessment and Decision Making

• Chapter 10: Introduction to Decision Theory
  • Elements of a Decision Problem: Actions, States, Consequences
  • Utility Theory and Loss Functions
  • Expected Utility and Bayes Actions
  • Risk Aversion and Risk Neutrality
• Chapter 11: Risk Assessment and Uncertainty Quantification
  • Quantifying Risk: Probability and Consequence
  • Sensitivity Analysis and Uncertainty Propagation
  • Incorporating Expert Judgment in Risk Assessment
  • Risk Aggregation
• Chapter 12: Optimal Maintenance and Inspection Policies
  • Bayesian Approach to Preventive Maintenance
  • Optimal Inspection Intervals
  • Replacement Policies
  • Condition-Based Maintenance
• Chapter 13: Reliability Growth and Software Reliability
  • Models for Reliability Growth (e.g., Duane Model, JM-DD Model)
  • Bayesian Inference for Reliability Growth Parameters
  • Software Reliability Models and Prediction
  • Testing and Debugging Strategies

Part IV: Computational and Advanced Topics

• Chapter 14: Computational Methods for Bayesian Inference
  • Markov Chain Monte Carlo (MCMC) Methods
  • Metropolis-Hastings Algorithm
  • Gibbs Sampling
  • Convergence Diagnostics
  • Software for Bayesian Computation (e.g., WinBUGS, R, Python Libraries)
• Chapter 15: Hierarchical Bayesian Models
  • Modeling Variability Across Groups
  • Bayesian Hierarchical Models for Reliability Data
  • Borrowing Strength Across Data Sets
• Chapter 16: Bayesian Nonparametrics in Reliability
  • Dirichlet Process Priors
  • Nonparametric Life Data Analysis
  • Mixture Models
• Chapter 17: Case Studies and Real-World Applications
  • Examples from Engineering, Aerospace, Healthcare, Finance
  • Practical Challenges and Solutions

People also search for Reliability and Risk A Bayesian Perspective 1st:

    
reliability and risk a bayesian perspective
    
bayesian reliability analysis
    
bayesian reliability
    
bayesian reliability model
    
bayesian risk analysis

 

Tags: Nozer D Singpurwalla, Reliability, Bayesian