There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This …
Covering a wide range of Random Graphs subjects, this volume examines series-parallel networks, properties of random subgraphs of the n-cube, …
For more than 35 years now, George B. Dantzig's Simplex-Method has been the most efficient mathematical tool for solving linear …
This title provides a comprehensive survey over the subject of probabilistic combinatorial optimization, discussing probabilistic versions of some of the …
Probabilistic graphical models is a technique in machine learning that uses the concepts of graph theory to concisely represent and …
This book is a systematic introduction to analytic methods in number theory, and assumes as a prerequisite only what is …
Abstract: The basic objective of a search is to identify an object and the position of the target. The target’s …
This book really helps in bridging formalism to understanding by providing lots of examples and walking through the examples. It's …
Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such …
