Financial Modelling in Python (The Wiley Finance Series) Review

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(More customer reviews)I am a quant, dealing with financial modeling on daily basis, and this book is the worst that I have ever used.
I had great expectations because I love python but I was so disappointed.
It is really complicated to use the CD and the explanations are so poor.
The book is mostly full with code without real explanations.
Don't buy this book and don't waste your money. It is very bad.
I dont like to write bad reviews, actually it is my first time but this book is very bad.

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"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims."–David Louton, Professor of Finance, Bryant University
This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language.
It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided.
Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point.
The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.
The book is accompanied by a CD ROM containing a code library; and a companion website www.wiley.com/go/fletcher_python which will feature code-based updates relating to Python 3.0.

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Stats: Modeling the World Review

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(More customer reviews)Chapter 2 part 1, i was reading about amazon and they put their item number in it describing about how 0321267435 may be a numerical value but it's actually the name Stats: Modeling the World. so i typed the code in and what do you know? I'm here now!

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Stats: Modeling the World is a modern book in many ways. It carries a core focus on statistical thinking throughout the text, emphasizing how statistics helps us to understand the world. And it utilizes both graphing calculator and computer software technologies in doing Statistics. The topic order is designed to ensure that each new topic fits into the growing structure of understanding that students build. The mantra of Think, Show, and Tell is repeated in every chapter, emphasizing the importance of thinking about a statistics question and reporting our findings. The authors know that the best way to teach is with humor. The book is fun to read. And students report that they actually read it. (Honest!)--This text refers to an out of print or unavailable edition of this title.

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Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing (Artech House Signal Processing Library) Review

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(More customer reviews)I believe this book is destined to become the "classic" graduate text used to teach statistical and adaptive digital signal processing.
If you enjoyed the introductory text "Digital Signal Processing" by Proakis and Manolakis (Prentice-Hall 1996), I think you will enjoy this book by Manolakis, Ingle, and Kogon. It is written in a similar style, with an introduction to each chapter previewing the material to be covered, a logical development of the material including examples, and a conclusion summarizing the high points of the material covered.
At chapter's end, there is a set of well thought out exercises ranging from easy to difficult. There are no answers to the problems in the back of the book, but there are enough examples in each chapter that one should be able to tackle most of the exercises. Some of the exercises require MATLAB. The authors have written some custom MATLAB functions which are available from the publisher as an e-Mail attachment.
I would say this book is written at the graduate level and requires knowledge of several disciplines: 1) DSP- At the level of Proakis + Manolakis intro text (cited above). 2) Linear Algebra- Cramer's rule, LDU factorization, eigenvalues, eigenvectors, Hermitian and Unitary matrices, etc. 3) Statistics- Random variables, averages, variances, estimators, sampling distributions, auto- and cross- correlations. I had no previous knowledge of stochastic processes, and was able to pick up enough from Chapter 3 to get through the rest of the book.
This book is, above all, a mathematical text written for engineers. It describes the theory and equations underlying statistical filters.
There is a lot of meat in each section. I typically had to read each section an average of 3 times for it to sink in.
With help from Figure 1.2.8 of the book, it covers the following material:
Chapter 1- Introduction to applications of spectral estimation, signal modeling, adaptive filtering, and array processing.
Chapter 2- Review of discrete-time signal processing.
Chapter 3- Review of random vectors and signals: properties, linear transformations, and estimation.
Chapter 4- Random signal models with rational system functions (AR, MA, ARMA, ARIMA).
Chapter 5- Nonparametric spectral estimation.
Chapter 6- Optimum filters and predictors -- matched filters (including Wiener) and eigenfilters.
Chapter 7- Algorithms and structures for optimum filtering (including algorithms of Levinson, Levinson-Durbin, Schur, Kalman, ...)
Chapter 8- Least-squares filtering and prediction (normal equations, orthogonalization, SVD).
Chapter 9- Signal modeling and parametric spectrum estimation.
Chapter 10- Adaptive filters: Design, performance, implementation, and applications (includes steepest descent, LMS, NLMS, CRLS, QR-RLS, fast RLS, fast Kalman, RLS lattice-ladder, ...).
Chapter 11- Array processing: theory, algorithms, and applications.
Chapter 12- Higher order statistics, blind deconvolution and equalization, fractional and fractal random signal models.
Appendix B includes the clearest, most graphic example of LaGrange multipliers I have ever seen!
Note that this book deliberately leaves out the following topics because it is NOT meant to be a text that covers ALL of ADVANCED DSP: Multirate DSP, Wavelets, etc.
I highly recommend this book to anyone involved in spectral estimation, signal modeling, adaptive filtering, or array processing.

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Signal processing is an essential topic for all practicing and aspiring electrical engineers to understand no matter what specific area they are involved in. Originally published by McGraw-Hill* and now reissued by Artech House, this definitive volume offers a unified, comprehensive and practical treatment of statistical and adaptive signal processing. Written by leading experts in industry and academia, the book covers the most important aspects of the subject, such as spectral estimation, signal modeling, adaptive filtering, and array processing. This unique resource provides balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike. The book presents clear examples, problem sets, and computer experiments that help readers master the material and learn how to implement various methods presented in the chapters. This invaluable reference also includes a set of Matlab[registered] functions that engineers can use to solve real-world problems in the field. The book is packed with over 3,000 equations and more than 300 illustrations.

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Bayesian Modeling Using WinBUGS (Wiley Series in Computational Statistics) Review

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(More customer reviews)I try to read at least a couple of statistics books every year and this was one of them for 2009. So far I have been really impressed. If you want a complete introduction to Bayesian statistics, then buy this book. You will find a balanced blend of theory, applications, and the use of the WinBUGS software package all under one roof. The book's treatment of models for count data is notable. Ntzoufras has a nice way of expressing himself that makes the reading move along. I would have no compunction at all about using this book to teach a M.S.-level course for statistics majors. If you are an ecologist, say, then you should probably have both a probability and mathematical statistics course under your belt to fully absorb all that is going on.

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A hands-on introduction to the principles of Bayesian modeling using WinBUGS
Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles.
The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including:

Markov Chain Monte Carlo algorithms in Bayesian inference

Generalized linear models

Bayesian hierarchical models

Predictive distribution and model checking

Bayesian model and variable evaluation

Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site.
Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.

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