Data Science Decisions in Time:Sequential Hypothesis Testing
Master advanced statistical methods for sequential hypothesis testing and dynamic decision-making.Data Science Decisions in Time:Sequential Hypothesis Testing
Master advanced statistical methods for sequential hypothesis testing and dynamic decision-making.Data Science Decisions in Time:Sequential Hypothesis Testing
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science Decisions in Time Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
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What you'll learn
Master sequential hypothesis testing techniques
Implement dynamic decision-making algorithms
Apply Chernoff's methods to real-world problems
Develop efficient testing strategies for large datasets
Skills you'll gain
This course includes:
2.8 Hours PreRecorded video
5 quizzes, 6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course explores advanced approaches to sequential hypothesis testing and dynamic decision-making. Starting with Chernoff's landmark paper, students learn modern applications including medical diagnostics, adaptive testing, and large-scale image analysis. The curriculum covers hierarchical searching, approximation algorithms, and applications in biology and medicine, emphasizing both theoretical foundations and practical implementations.
Chernoff and Active Hypothesis Testing
Module 1 · 4 Hours to complete
Hierarchical Searching for Alternative Hypothesis
Module 2 · 4 Hours to complete
Large Hypothesis and/or Action Spaces
Module 3 · 4 Hours to complete
Sequential Hypothesis for Biology and Medicine
Module 4 · 4 Hours to complete
Putting it together: testing on visual images
Module 5 · 4 Hours to complete
Untitled Module
Module 6 · 3 Hours to complete
Fee Structure
Instructor
Distinguished Biophysicist and Computational Science Leader at Johns Hopkins
Dr. Thomas Woolf serves as a Professor at Johns Hopkins University School of Medicine since 1994, bringing expertise in biophysics and computational science. After earning his Ph.D. in Biophysics from Yale University and B.S. in Physics from Stanford University, he has established himself as a leader in membrane protein research and computational biophysics. His work combines high-performance computing, machine learning, and molecular dynamics to understand complex biological systems. As director of his research lab, he focuses on studying membrane proteins using advanced computational methods and the molecular dynamics program CHARMM. Beyond his academic work, Dr. Woolf is also CEO and co-founder of DaiWare, Inc., a healthcare company focusing on patient data interpretation using streaming data and machine learning technologies. He teaches courses in stochastic differential equations, probabilistic graphical models, and statistics, while conducting research in time-series analysis, cellular biophysics, and metabolic processes.
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