INSTRUCTOR: PIERO BARALDI
LECTURES
Lecture 1: Introduction to the Course
Lecture 2: Interpretation of Probability in Reliability and Risk Analysis
Lecture 4: Basic Notions of Probability Theory, Continuous Probability Distributions
Lecture 5: Reliability of Simple Systems
Lecture 6: Availability and Maintainability
Lecture 7: Markov Discrete Time
Lecture 8: Markov Continuous Time
Lecture 9: Monte Carlo Simulation
Lecture 10: Parameter Estimation
Lecture 11: Parameter Estimation Bayes Approach
Lecture 12: Monte Carlo Simulation for Definite Integrals
Lecture 14: Reinforcement Learning for Maintenance
Lecture 16: Fault Tree Analysis
Lecture 17: Event Tree Analysis
Lecture 18: Importance Measures
Lecture 19: Dependent Failures
Lecture 20: Seminar “Risk Assessment for the Future: Challenges and Directions for the Research”
EXERCISES SESSIONS
Exercise Session 1: Basics of Probability theory
Exercise Session 2: Availability and Maintainability
Exercise Session 3: Monte Carlo Simulation
Exercise Session 4: Markov Chains
Exercise Session 5: Exam Simulation
Exercise Session 6: Fault Tree Analysis
Exercise Session 7: Event Tree Analysis
FLIPPED CLASS
Flipped Class 2: Basic Concepts
ADDITIONAL MATERIAL
Table of Standard Normal Probability
Laplace Transform for Reliability Applications
Chapter: Introducing Bayesian Networks
Bayesian Parameter Estimation: Exercises
VIDEOS