Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as "Monte Carlo." The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear engineering, which are illustrative of uses in other fields. Five appendices are included, which provide useful information on probability distributions, general-purpose Monte Carlo codes for radiation transport, and other matters. The famous "Buffon s needle problem" provides a unifying theme as it is repeatedly used to illustrate many features of Monte Carlo methods.

This book provides the basic detail necessary to learn how to apply Monte Carlo methods and thus should be useful as a text book for undergraduate or graduate courses in numerical methods. It is written so that interested readers with only an understanding of calculus and differential equations can learn Monte Carlo on their own. Coverage of topics such as variance reduction, pseudo-random number generation, Markov chain Monte Carlo, inverse Monte Carlo, and linear operator equations will make the book useful even to experienced Monte Carlo practitioners.
Provides a concise treatment of generic Monte Carlo methods
Proofs for each chapter
Appendixes include Certain mathematical functions; Bose Einstein functions, Fermi Dirac functions, Watson functions"

This book is devoted to the study of stochastic measures (SMs). An SM is a sigma-additive in probability random function, defined on a sigma-algebra of sets. SMs can be generated by the increments of random processes from many important classes such as square-integrable martingales and fractional Brownian motion, as well as alpha-stable processes. SMs include many well-known stochastic integrators as partial cases.General Stochastic Measures provides a comprehensive theoretical overview of SMs, including the basic properties of the integrals of real functions with respect to SMs. A number of results concerning the Besov regularity of SMs are presented, along with equations driven by SMs, types of solution approximation and the averaging principle. Integrals in the Hilbert space and symmetric integrals of random functions are also addressed.The results from this book are applicable to a wide range of stochastic processes, making it a useful reference text for researchers and postgraduate or postdoctoral students who specialize in stochastic analysis.

The valuation of the liability structure can be determined by real options because the shares of a company can be regarded as similar to the purchase of a financial call option. Therefore, from this perspective, debt can be viewed as the sale of a financial put option. As a result, financial analysts are able to establish different valuations of a company, according to these two financing methods. Valuation of the Liability Structure by Real Options explains how the real options method works in conjunction with traditional methods. This innovative approach is particularly suited to the valuation of companies in industries where an underlying asset has high volatility (such as the mining or oil industries) or where research and development costs are high (for example, the pharmaceutical industry). Integration of the economic value of net debt (rather than the accounting value) and integration of the asset volatility are the main advantages of this approach.

These materials were developed, in part, by a grant from the federally-funded Mathematics and Science Partnership through the Center for STEM Education. Some of the activities were adapted from the National Council of Teachers of Mathematics Illuminations, the National Library of Virtual Manipulatives, Hands-On Math Projects with Real Applications by Judith A. Muschla and Gary R. Muschla, Learning Math with Calculators: Activities for Grades 3-8 by Len Sparrow and Paul Swan, and Mathematical Ideas by Charles D. Miller, Vern E. Heeren and John Hornsby.