Explore a Kinetic Approach to the Description of Nucleation - An Alternative to the Classical Nucleation Theory Kinetic Theory of Nucleation presents an alternative to the classical theory of nucleation in gases and liquids-the kinetic nucleation theory of Ruckenstein-Narsimhan-Nowakowski (RNNT). RNNT uses the kinetic theory of fluids to calculate the rate of evaporation of molecules from clusters, and unlike the classical nucleation theory (CNT), does not require macroscopic thermodynamics or the detailed balance principle. The book compares the rates of evaporation of molecules from-and condensation on-the surface of a nucleus of a new phase, and explains how this alternate approach can provide much higher nucleation rates than the CNT. It applies RNNT to various case studies that include the liquid-to-solid and vapor-to-liquid phase transitions, binary nucleation, heterogeneous nucleation, nucleation on soluble particles and protein folding. It also describes the system, introduces the basic equations of the kinetic theory, and defines a new model for the nucleation mechanism of protein folding. Adaptable to coursework as well as self-study, this insightful book: Uses a kinetic approach to calculate the rate of growth and decay of a cluster Includes description of vapor-to-liquid and liquid-to-solid nucleation Outlines the application of density-functional theory (DFT) methods to nucleation Proposes the combination of the new kinetic theory of nucleation with the DFT methods Illustrates the new theory with numerical calculations Describes the model for the nucleation mechanism of protein folding, and more A comprehensive guide dedicated to the kinetic theory of nucleation and cluster growth, Kinetic Theory of Nucleation emphasizes the basic concepts of the kinetic nucleation theory, incorporates findings developed from years of research and experience, and is written by highly-regarded experts.

This volume represents the proceedings of the Ninth Annual MaxEnt Workshop, held at Dartmouth College in Hanover, New Hampshire, on August 14-18, 1989. These annual meetings are devoted to the theory and practice of Bayesian Probability and the Maximum Entropy Formalism. The fields of application exemplified at MaxEnt '89 are as diverse as the foundations of probability theory and atmospheric carbon variations, the 1987 Supernova and fundamental quantum mechanics. Subjects include sea floor drug absorption in man, pressures, neutron scattering, plasma equilibrium, nuclear magnetic resonance, radar and astrophysical image reconstruction, mass spectrometry, generalized parameter estimation, delay estimation, pattern recognition, heave responses in underwater sound and many others. The first ten papers are on probability theory, and are grouped together beginning with the most abstract followed by those on applications. The tenth paper involves both Bayesian and MaxEnt methods and serves as a bridge to the remaining papers which are devoted to Maximum Entropy theory and practice. Once again, an attempt has been made to start with the more theoretical papers and to follow them with more and more practical applications. Papers number 29, 30 and 31, by Kesaven, Seth and Kapur, represent a somewhat different, perhaps even "unorthodox" viewpoint, and are included here even though the editor and, indeed many in the audience at Dartmouth, disagreed with their content. I feel that scientific disagreements are essential in any developing field, and often lead to a deeper understanding.

Here is a rigorous introduction to the most important and useful solution methods of various types of stochastic control problems for jump diffusions and its applications. Discussion includes the dynamic programming method and the maximum principle method, and their relationship. The text emphasises real-world applications, primarily in finance. Results are illustrated by examples, with end-of-chapter exercises including complete solutions. The 2nd edition adds a chapter on optimal control of stochastic partial differential equations driven by Levy processes, and a new section on optimal stopping with delayed information. Basic knowledge of stochastic analysis, measure theory and partial differential equations is assumed."

This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.