Please use this identifier to cite or link to this item:
http://localhost:8080/xmlui/handle/123456789/734| Title: | Probability and Statistics for Computer Science/ |
| Authors: | Forsyth, David |
| Keywords: | Probability theory Statistics Computer science Data analysis |
| Issue Date: | 2018 |
| Publisher: | Springer |
| Abstract: | This book focuses on the fundamental principles of probability and statistics and their applications in computer science. It introduces concepts such as probability theory, random variables, probability distributions, statistical analysis, estimation, and inference. These concepts help computer science students and researchers understand uncertainty, analyse data, develop algorithms, and evaluate computational systems. The subject is relevant to areas such as machine learning, artificial intelligence, data science, computer vision, and algorithm design. The book provides a mathematical foundation for solving computational problems and making informed decisions based on data. |
| Description: | 363 pages: illustrations (some color), includes an index |
| URI: | http://localhost:8080/xmlui/handle/123456789/734 |
| ISBN: | 978-3-319-64410-3 |
| ISSN: | 978-3-319-64409-7 |
| Appears in Collections: | ICT |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Probability and Statistics for Computer Science.pdf | 6 MB | Adobe PDF | View/Open |
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