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Free Download Computational Statistics in Java
English | 2025 | ASIN: B0F2SHYCPC | 693 Pages | PDF | 17.29 MB
In this book, we aimed to address the topic of computational statistics, which is so important for the scientific community and, especially, for students in graduate programs in statistics. We can confidently state that computational statistics has become, and currently is, one of the main areas of statistics. Moreover, the knowledge in this area can be, and often is, applied in other fields of statistics, engineering, and physics. Bayesian inference is a typical example where a computational approach is generally used. When we conceived this book, we had many doubts about what to cover and how to approach each chosen topic. Thus, we opted to write a book that would allow the reader to go beyond a simple set of instructions, without getting lost in a maze of proofs. On the other hand, we sought to present models and methods in a comprehensive and non-restrictive manner.
Another motivation that guided and encouraged us to develop this project was the experience we gained through research that utilizes the concepts and methods of computational statistics. With accumulated experience and knowledge in this area, we felt the need to share and disseminate everything we had learned so far with our students, avoiding the hedonism that often plagues professors and researchers. We also did this thinking about the personal benefit, which we cannot deny, that it would bring us by directly engaging with the vast literature in this branch of statistics. However, we do not intend to cover all topics in this book, nor do we aim to exhaust all possibilities for any specific topic. On the contrary, we hope this book serves as an introduction to computational statistics and motivates undergraduate and graduate students in statistics to delve deeper into this field.
Wishing to deepen our knowledge of the wonderful programming language Java, we sought to write numerous methods and classes for the various topics covered in this work. We hope this book becomes a good option for professionals in computational statistics and can also be adopted in undergraduate and graduate statistics courses as a textbook for computational statistics. In this way, our objective will have been achieved.
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