2021/12/22 by C. W. Gardiner · 1 citation
Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Engineering · Mathematics · Physics and Astronomy · #Applied mathematics #Brownian motion #Calculus (dental) #Computer science #Diffusion and Search Dynamics #Engineering #Field (mathematics) #Jump #Machine learning #Management science #Markov chain #Markov process #Mathematical analysis #Mathematics #Partial differential equation #Physics #Pure mathematics #Range (aeronautics) #Statistical physics #Stochastic calculus #Stochastic differential equation #Stochastic partial differential equation #Stochastic process #Stochastic processes and financial applications #Theoretical computer science #stochastic dynamics and bifurcation
paper · pdf · doi:10.1063/9780735423718
openalex publication_date 2021/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
This book provides a compact practical exposition of the stochastic methods and their applications for a wide range of fields, including population dynamics, infectious diseases, financial modeling, hydrology, physics, quantum optics, chemistry, and many others. The aim is to give practitioners an idea of how to formulate their current problem stochastically, and how to get an idea of what the predictions of such modeling are. Elements of Stochastic Methods features:A treatment based on Markov processes, including an outline of their theoretical foundations, and a description of basic algorithms for simulating Markov processesA straightforward and practical formulation of stochastic differential equations and Ito calculus, including simulation methodsHeavy-tailed processes, Mandelbrot's fractional Brownian motion, as well as the more traditional diffusion and jump processes This book is an essential resource for researchers, professionals, and graduate students in physics and in any field where random processes are relevant.