Introduction to Model Development for Prediction, Simulation and Optimization
Published on by Mrinmoy Majumder, Associate Professor at National Institute of Technology Agartala for Baipatra : Aim for Sustainability
The purpose of this book, which may be compared to an online course, is to offer students with a full understanding of the process of developing statistical models. Participants will acquire the information and skills essential to apply statistical models for the purposes of prediction, simulation, and optimisation through the use of detailed tutorials and complete notes. This course will provide you with the tools and strategies necessary to efficiently create statistical models in a variety of domains, regardless of whether you are a novice or an experienced practitioner. In addition to this, this book on Paper to PC has a total of one hundred case studies, one hundred numerical questions, and fifty different project ideas.
The content is :
Introduction to Model Development for Prediction, Simulation and Optimization
1) Introduction
1.1. Model Development for Prediction, Simulation and Optimization
1.2. Common Challenges in Model Development
2) Outlier Detection
2.1. Chauvenet Method
2.2. Dixon Thompson Method
2.3. Rosner’s Method
3) Trend Detection
3.1. What is Trend?
3.2. Runs Test
4) Auto and Cross-Correlation
4.1. Auto and Cross-Correlation
4.2. Auto and Cross Regression Model
5) Risk analysis
5.1. What is Risk and Vulnerability?
5.2. Weibull’s Method
5.3. Numerical Analysis
6) Uncertainty Analysis
6.1. Definition
6.2. Procedure
6.3. Standard Uncertainty and Relative Standard Uncertainty
6.4. Total Percent Uncertainty
6.5. Uncertainty and standard deviation are the same?
6.6. What is uncertainty in decision-making?
6.7. List of statistical functions used for uncertainty analysis
7) Development of the Regression Models
7.1. Linear Regression Models
7.2. Non-Linear Regression Models
8) Types of Model Development
8.1. How to use the model for Prediction?
8.2. How to use the model for Simulation?
8.3 How to use the model for Optimization?
9) Performance Metrics
9.1. Regression Error Identification Metrics
9.2. Classification Error Identification Metrics
9.3. Correlation Identification Metrics
9.4. Efficiency Identification Metrics
9.5. Reliability Analysis
10) Project Ideas: 50
11) Numerical Problems: 100
12) Case Studies: 100
Attached link
https://notionpress.com/read/introduction-to-model-development-for-prediction-simulation-and-optimizationTaxonomy
- Standards & Quality
- Standards
- Stakeholder Engagement
- hydrometry and gauging stations