Analyzing Data Through Probabilistic Modeling in Statistics

Book Description Probabilistic modeling represents a subject arising in many branches of mathematics, economics, and computer science. Such modeling connects pure mathematics with applied sciences. Similarly, data analyzing and statistics are situated on the border between pure mathematics and applied sciences. Therefore, when probabilistic modeling meets statistics, it is a very interesting occasion that has gained much research recently. With the increase of these technologies in life and work, it has become somewhat essential in the workplace to have planning, timetabling, scheduling, decision making, optimization, simulation, data analysis, and risk analysis and process modeling. However, there are still many difficulties and challenges that arrive in these sectors during the process of planning or decision making. There continues to be the need for more research on the impact of such probabilistic modeling with other approaches. Analyzing Data Through Probabilistic Modeling in Statistics is an essential reference source that builds on the available literature in the field of probabilistic modeling, statistics, operational research, planning and scheduling, data extrapolation in decision making, probabilistic interpolation and extrapolation in simulation, stochastic processes, and decision analysis. This text will provide the resources necessary for economics and management sciences and for mathematics and computer sciences. This book is ideal for interested technology developers, decision makers, mathematicians, statisticians and practitioners, stakeholders, researchers, academicians, and students looking to further their research exposure to pertinent topics in operations research and probabilistic modeling.

Content

Section 1 - Probabilistic Modeling in Statistics
Chapter 1. Determination of Poverty Indicators Using Roc Curves in Turkey
Chapter 2. Data Analyzing via Probabilistic Modeling: Interpolation and Extrapolation
Chapter 3. Decision Making and Data Analysis: Curve Modeling via Probabilistic Method

Section 2 - Dual Approach of Data Analytics and Machine Learning Modelling in Real Case Scenarios
Chapter 4. Patient Arrival to Public OPDs: Analysis and Use of Statistical Distribution for Improving Performance Indicators in Rural Hospitals
Chapter 5. An Econometric Overview on Growth and Impact of Online Crime and Analytics View to Combat Them
Chapter 6. A Decadal Walk on BCI Technology: A Walkthrough
Chapter 7. A Fusion-Based Approach to Generate and Classify Synthetic Cancer Cell Image Using DCGAN and CNN Architecture
Chapter 8. The Rise of “Big Data” in the Field of Cloud Analytics

Section 3 - Case Studies From Business and Industry
Chapter 9. Analyzing EPQ Inventory Model With Comparison of Exponentially Increasing Demand and Verhult’s Demand
Chapter 10. Statistics of an Appealing Class of Random Processes
Chapter 11. The Universality of the Kalman Filter: A Conditional Characteristic Function Perspective
Chapter 12. Project Control: A Bayesian Model