Imaging Science Seminar: Unveiling the Mystique of Bayesian Thinking
Imaging Science Seminar
Unveiling the Mystique of Bayesian Thinking: A Hands-on Dive with R and Python
Dr. Ernest Fokoué
Professor
School of Mathematics and Statistics, RIT
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Abstract:
Data science, Data mining, Data Analytics … It’s all about data these days!! Data is everywhere, yet making sense of it all can be challenging. A wide variety of approaches have been used to make the most of the potential underlying data. Among the most commonly used, the one motivating our presentation is the Bayesian paradigm - a powerful approach that blends data with prior knowledge, yielding insights with profound implications. This interactive 45-minute session invites you to dive into the captivating world of Bayesian statistics, tailored for novices and seasoned professionals alike. Our journey will commence with the fundamental principles of the Bayesian approach, distinguished from traditional frequentist methods. Next, we'll delve into Bayesian inference, modeling, and estimation, elucidating concepts with engaging examples in both R and Python. The voyage will reach its zenith with the gripping case of Air France Flight 447, demonstrating how Bayesian methods helped uncover the wreckage lost in the depths of the ocean - a testament to the paradigm's potency and versatility. Join us as we demystify Bayesian statistics, traverse through real-world examples, and unleash the power of probabilistic thinking in the realm of data science. This hands-on session will leave you with an enriched understanding and the skills to apply Bayesian methods to your own data puzzles. Get ready to step into the Bayesian landscape, where data tells a story and prior knowledge is the plot.
Speaker Bio:
Ernest Fokoue is Professor in the School of Mathematical Sciences at Rochester Institute of Technology. He earned his PhD in Statistics at the University of Glasgow in the United Kingdom. He was postdoctoral research fellow at the Statistical and Applied Mathematical Sciences Institute (SAMSI). Prior to joining Rochester Institute of Technology, he held faculty positions at The Ohio State University and Kettering University. He is co-author of the Springer graduate textbook “Principles and Theory for Data Mining and Machine Learning”, and has been intensively and extensively involved in the interface between statistical science and artificial intelligence. He is a strong advocate of a complete approach to statistical machine learning and data science comprising a non-degenerate coverage of applications, computation, methodology and theory (ACMT), the staple of his research and teaching. A passionate lover of mathematical sciences from his earliest childhood, he often summarizes his philosophy as follows: “while the letter of mathematics provides great delight to the seeker/researcher/practitioner, the spirit of this mother of all disciplines bestows experiences of a transcendental, nay ineffable nature. Therefore, seek the latter not the former.”
Intended Audience:
Undergraduates, graduates, and experts. Those with interest in the topic.
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