Bayesian Model Updating with Matlab
Course specifications
Duration: One session (1 hour)
Software Used: MATLAB
Prerequisites: Basic mathematics + MATLAB programming
Instructor: Dr. Mahdi Shadabfar
Course Fee: €50
Course Fee: €50
Course description
In recent years, Bayesian modeling has become one of the most widely used approaches in various fields, including data science, artificial intelligence, engineering, economics, and medicine. Numerous studies have been conducted in this area, and many students and researchers have turned to working with Bayesian models using a variety of approaches and tools.
In response to the needs of my students and audiences, I have prepared a clear, practical, and hands-on training for working with Bayesian models, which is presented here.
The course begins with an introduction to the fundamentals of Bayesian modeling, highlighting Bayesian Updating as one of its most widely applied research topics. A real-world engineering example is then presented, demonstrating how Bayesian modeling can be used to update prior knowledge by incorporating observed data.
Beyond updating prior knowledge with data through Bayesian modeling, this training also emphasizes the foundational concepts of the approach and its practical implementation in MATLAB. The entire workflow is thoroughly explained and coded step by step from beginning to end.
In response to the needs of my students and audiences, I have prepared a clear, practical, and hands-on training for working with Bayesian models, which is presented here.
The course begins with an introduction to the fundamentals of Bayesian modeling, highlighting Bayesian Updating as one of its most widely applied research topics. A real-world engineering example is then presented, demonstrating how Bayesian modeling can be used to update prior knowledge by incorporating observed data.
Beyond updating prior knowledge with data through Bayesian modeling, this training also emphasizes the foundational concepts of the approach and its practical implementation in MATLAB. The entire workflow is thoroughly explained and coded step by step from beginning to end.
Course content
Session 1: Understanding time series and their processing techniques
Overview of Bayesian Updating fundamentals
Presentation of a civil engineering example
Introduction to the necessary steps for solving the problem, including:
o Initialization and Setup
o Deterministic Model
o Prior Distributions
o Likelihood Function
o Posterior Distribution
o Sampling from Posterior using MCMC
o MCMC Convergence Plot
o Posterior Distribution Graph
Implementation of all steps in MATLAB with line-by-line coding
Extracting results and plotting the outputs
Further Information
If you’re looking for a concise and focused course on Bayesian Model Updating, this pre-recorded session is tailored for you. After purchasing the course through the provided botton below, you’ll receive an email containing the training video, class notes, and the MATLAB codes used throughout the session.
Registration
To purchase the course, please click the bottom below and fill in your details, including your first name, last name, email address, phone number, and your country of residence. After completing the payment of €50, you will receive an email shortly (within a few hours) with access to the course materials—this includes the recorded video, class notes, and all relevant MATLAB codes.
Enroll the Course
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