Institute of Mathematical Statistics Textbooks Series

Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of It� calculus, the central theorems in the field, and such approximation schemes as stochastic Runge-Kutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book's practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-driven derivations and computational assignments. MATLAB/Octave source code is available for download, promoting hands-on work with the methods.

Exploring the longest increasing subsequence problem reveals its intriguing connections to diverse mathematical fields, including random permutations and matrices. This book offers a playful yet detailed examination of these links, making complex concepts accessible to graduate students in mathematics, computer science, physics, and statistics. Key topics include the Vershik-Kerov-Logan-Shepp theorem, the Baik-Deift-Johansson theorem, and the Tracy-Widom distribution, showcasing significant advances in probability and combinatorics over the past four decades.