INSTRUCTOR: PIERO BARALDI


LECTURES

Lecture 1: Introduction to the Course

Lecture 2: Interpretation of Probability in Reliability and Risk Analysis

Lecture 3: Flipped Class 1

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 13: Maintenance

Lecture 14: Reinforcement Learning for Maintenance

Lecture 15: Risk

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

Exam Simulation 30-05-2025

Exam Simulation 30-05-2025


FLIPPED CLASS

Flipped Class 1: Exercises

Flipped Class 1: Solutions

Flipped Class 2: Basic Concepts

Flipped Class 2: Exercises

Flipped Class 2: Solutions


ADDITIONAL MATERIAL

Table of Standard Normal Probability

Laplace Transform for Reliability Applications

Chapter: Introducing Bayesian Networks

Importance Measures: Exercise

Bayesian Parameter Estimation: Exercises


VIDEOS