Probabilistic Reasoning in AI
| Institution | Jomo Kenyatta University of Science and Technology |
| Course | Information Technolo... |
| Year | 3rd Year |
| Semester | Unknown |
| Posted By | Jeff Odhiambo |
| File Type | |
| Pages | 6 Pages |
| File Size | 266.69 KB |
| Views | 3946 |
| Downloads | 0 |
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Description
Buy "Probabilistic Reasoning in AI" now and learn how to navigate the uncertainty inherent in real-world scenarios. This insightful book explores the causes of uncertainty, from unreliable sources to equipment faults, and introduces probabilistic reasoning as a robust method for knowledge representation. By integrating probability theory with logic, the book provides a comprehensive approach to handling uncertain knowledge, making it an invaluable resource for both beginners and seasoned professionals in the field of artificial intelligence.
Discover the essential techniques of probabilistic reasoning, such as Bayes' rule and Bayesian statistics, that enable AI systems to make informed decisions despite unpredictable outcomes. The book covers key concepts like prior and posterior probabilities, conditional probability, and random variables, with practical examples that illustrate their applications in various AI-driven tasks. "Probabilistic Reasoning in AI" is an essential read for anyone looking to deepen their understanding of AI's reasoning processes and effectively apply these techniques to solve complex real-world problems.
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SMA 2371: Partial Differential Equations (Complete Notes, Examples & Solutions) – JKUAT Biostatistics Year 2 Semester 2
These are comprehensive and well-organized lecture notes for SMA 2371: Partial Differential Equations (PDE) offered to BSc. Biostatistics Year 2 Semester 2 students at JKUAT. The notes are neatly arranged from lecture one to the final topics, making them ideal for class learning, revision, CAT preparation, and final examinations.
The notes include detailed explanations, worked examples, step-by-step mathematical derivations, solved exercises, and applications of Partial Differential Equations. Topics covered include:
• Review of basic concepts and partial derivatives
• Jacobians, surfaces and curves in three dimensions
• Simultaneous first-order differential equations
• Methods of solving symmetric differential equations
• Orthogonal trajectories
• Pfaffian differential equations
• Linear first-order partial differential equations
• Formation of PDEs
• Elimination of arbitrary constants and arbitrary functions
• Heat, Wave, Laplace and Poisson equations
• Separation of variables
• Fourier and Laplace Transform methods
• Numerous worked examples, tutorial questions and examination-style problems with solutions.
These notes are suitable for JKUAT students and other university students studying Mathematics, Statistics, Biostatistics, Engineering, Applied Mathematics or related courses. They are an excellent revision resource for CATs and final examinations.
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