The theory of stochastic integration, also called the Ito calculus, has a large spectrum of applications in virtually every ...
WeiterlesenLévy processes are the natural continuous-time analogue of random walks and form a rich class of stochastic processes around ...
WeiterlesenLévy processes are the natural continuous-time analogue of random walks and form a rich class of stochastic processes around ...
WeiterlesenKanban control systems bear a great potential to significantly improve operations. A company may reap the full benefits of ...
WeiterlesenLagrangian expansions can be used to obtain numerous useful probability models, which have been applied to real life situations ...
WeiterlesenLagrangian expansions can be used to obtain numerous useful probability models, which have been applied to real life situations ...
WeiterlesenThis book describes in detail the practice of the Bayesian statistical approach using many examples chosen for their educational ...
WeiterlesenSince its inception in 1974, the level crossing approach for analyzing a large class of stochastic models has become increasingly ...
Weiterlesenincludes contributions originating from a conference held at Chapman University during November 14-19, 2017. It presents ...
WeiterlesenThis book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and ...
WeiterlesenThe aim of this book is to give a systematic introduction to and overview of the relatively simple and popular linearization ...
WeiterlesenWhen applying the statistical theory of long range dependent (LRD) processes to economics, the strong complexity of macroeconomic ...
WeiterlesenThe book provides a detailed introduction to maintenance policies, updates the reader on the current status of the field ...
WeiterlesenWhile the original works on Malliavin calculus aimed to study the smoothness of densities of solutions to stochastic differential ...
WeiterlesenMarkov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems ...
WeiterlesenMarkov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems ...
WeiterlesenMarkov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be ...
WeiterlesenThis book provides a comprehensive, self-contained and up-to-date treatment of the main topics in the theory of option pricing. ...
WeiterlesenThis book highlights recent developments in mathematical control theory and its applications to finance. It presents a collection ...
WeiterlesenBased on lecture notes of two summer schools with a mixed audience from mathematical sciences, epidemiology and public health, ...
Weiterlesen