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Leuzzi L: Probability Theory for
Quantitative Scientists

Probability Theory for Quantitative Scientists

The Converse of Bayes Theorem with Applications

Series in Computational Biophysics

Multiscale methods are ideally suited to simulate the processes at work in many biomolecular systems, which often span a large range of time and length scales. Multiscale Methods in Molecular Biophysics highlights the advantages of these methods for integrating different levels of detail in a single simulation setting. It covers the practical methodology and successful applications in areas such as enzyme catalysis, protein folding, and ligand binding. The book is divided into three main sections: combination quantum and classical (atomistic) descriptions, approaches that combine atomistic and coarse-grained level of details, and multiscale methods where the resolution coupling is between global collective coordinates and local details.

Multiscale methods are ideally suited to simulate the processes at work in many biomolecular systems, which often span a large range of time and length scales. Multiscale Methods in Molecular Biophysics highlights the advantages of these methods for integrating different levels of detail in a single simulation setting. It covers the practical methodology and successful applications in areas such as enzyme catalysis, protein folding, and ligand binding. The book is divided into three main sections: combination quantum and classical (atomistic) descriptions, approaches that combine atomistic and coarse-grained level of details, and multiscale methods where the resolution coupling is between global collective coordinates and local details.

Multiscale Methods in Molecular Biophysics

Franz Buscha's book, Graphs Everyone Should Know and How to Create Them in Stata, is for anyone who uses Stata to make graphs. Beginners will find a complete collection of tools for effectively visualizing their data and results. Experienced Stata users are certain to learn some new tricks as well.

Graphs Everyone Should Know and How to Create Them in Stata

De Gruyter Textbook

Financial Mathematics with Matlab®

Stochastic Analysis

Meta-analysis Using R

De Gruyter Studies in Mathematics

Orthogonal Polynomials

This text focuses on the parts of stochastic theory that are particularly relevant to applications. It begins with a description of Brownian motion and the associated stochastic calculus, including the relationship to partial differential equations. It then solves stochastic differential equations by a variety of methods. The author also studies in detail the one-dimensional case. The book concludes with a treatment of semigroups and generators, applying the theory of Harris chains to diffusions as well as weak convergence of Markov chains to diffusions.

This text focuses on the parts of stochastic theory that are particularly relevant to applications. It begins with a description of Brownian motion and the associated stochastic calculus, including the relationship to partial differential equations. It then solves stochastic differential equations by a variety of methods. The author also studies in detail the one-dimensional case. The book concludes with a treatment of semigroups and generators, applying the theory of Harris chains to diffusions as well as weak convergence of Markov chains to diffusions.

An Elementary Introduction Emphasizing Applications

Stochastic Calculus

Introduction to Numerical Modeling in the Earth Sciences

Principles of Nano-Optics

Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with an emphasis on developments to inference functions for use in complex data. These inference functions are usually motivated by considerations related to likelihood-type arguments and have a variety of names, including composite likelihood, quasi-likelihood and pseudo-likelihood.

Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with an emphasis on developments to inference functions for use in complex data. These inference functions are usually motivated by considerations related to likelihood-type arguments and have a variety of names, including composite likelihood, quasi-likelihood and pseudo-likelihood.

Likelihood and its Extensions

Advances in Quantum Chemistry

A Snapshot of Molecular Electronic Structure Theory and its Applications: Volume 92

Probability Theory for Quantitative Scientists

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