Sam Howison's Books

Practical Applied Mathematics

Modelling, Analysis, Approximation

By: Sam Howison

Write a review Read reviews Take a Quiz Solve Book Puzzle

Practical Applied Mathematics

Modelling, Analysis, Approximation

By: Sam Howison

Drawing from a wide variety of mathematical subjects, this book aims to show how mathematics is realised in practice in the everyday world. Dozens of applications are used to show that applied mathematics is much more than a series of academic calculations. Mathematical topics covered include distributions, ordinary and partial differential equations, and asymptotic methods as well as basics of modelling. The range of applications is similarly varied, from the modelling of hair to piano tuning, egg incubation and traffic flow. The style is informal but not superficial. In addition, the text is supplemented by a large number of exercises and sideline discussions, assisting the reader's grasp of the material. Used either in the classroom by upper-undergraduate students, or as extra reading for any applied mathematician, this book illustrates how the reader's knowledge can be used to describe the world around them.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

362

Publisher

Cambridge University Press

Published Date

ISBN 10

0521842743

ISBN 13

9780521842747

The Mathematics of Financial Derivatives

A Student Introduction

By: Paul Wilmott,Sam Howison,Jeff Dewynne

Write a review Read reviews Take a Quiz Solve Book Puzzle

The Mathematics of Financial Derivatives

A Student Introduction

By: Paul Wilmott,Sam Howison,Jeff Dewynne

Basic option theory - Numerical methods - Further option theory - Interest rate derivative products.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

338

Publisher

Cambridge University Press

Published Date

ISBN 10

0521497892

ISBN 13

9780521497893

Jump

Black Anarchism and Antiblack Carcerality

By: Sam C. Tenorio

Write a review Read reviews Take a Quiz Solve Book Puzzle

Jump

Black Anarchism and Antiblack Carcerality

By: Sam C. Tenorio

Interrupting our political orthodoxies and engaging an alternative origin story of the modern carceral state, Jump attends to the disruptions of confinement that constitute the racial and gendered hierarchies of the antiblack world and proposes a black anarchist politics of refusal that helps us to think dissent anew"--

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

208

Publisher

NYU Press

Published Date

ISBN 10

1479828297

ISBN 13

9781479828296

Financial Data Analytics with Machine Learning, Optimization and Statistics

By: Sam Chen,Ka Chun Cheung,Phillip Yam

Write a review Read reviews Take a Quiz Solve Book Puzzle

Financial Data Analytics with Machine Learning, Optimization and Statistics

By: Sam Chen,Ka Chun Cheung,Phillip Yam

An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

823

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119863376

ISBN 13

9781119863373

Practical Applied Mathematics

Modelling, Analysis, Approximation

By: Sam Howison

Write a review Read reviews Take a Quiz Solve Book Puzzle

Practical Applied Mathematics

Modelling, Analysis, Approximation

By: Sam Howison

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

362

Publisher

Cambridge University Press

Published Date

ISBN 10

0521842743

ISBN 13

9780521842747

The Mathematics of Financial Derivatives

A Student Introduction

By: Paul Wilmott,Sam Howison,Jeff Dewynne

Write a review Read reviews Take a Quiz Solve Book Puzzle

The Mathematics of Financial Derivatives

A Student Introduction

By: Paul Wilmott,Sam Howison,Jeff Dewynne

Finance is one of the fastest growing areas in the modern banking and corporate world. This, together with the sophistication of modern financial products, provides a rapidly growing impetus for new mathematical models and modern mathematical methods; the area is an expanding source for novel and relevant 'real-world' mathematics. In this book the authors describe the modelling of financial derivative products from an applied mathematician's viewpoint, from modelling through analysis to elementary computation. A unified approach to modelling derivative products as partial differential equations is presented, using numerical solutions where appropriate. Some mathematics is assumed, but clear explanations are provided for material beyond elementary calculus, probability, and algebra. Over 140 exercises are included. This volume will become the standard introduction to this exciting new field for advanced undergraduate students.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

338

Publisher

Cambridge University Press

Published Date

ISBN 10

1139810979

ISBN 13

9781139810975

实用数学

建模、分析、逼近

By: Sam Howison

Write a review Read reviews Take a Quiz Solve Book Puzzle

实用数学

建模、分析、逼近

By: Sam Howison

本书内容分为三部分:建模,讲述了建模的一些原则(包括物理定律、本构关系及守恒定律),量纲分析等;分析技巧,讲述了偏微分方程和广义函数的基础知识;渐近分析,讲述了渐近展开的基本概念,正则摄动展开,边界层和多重尺度法等。

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

326

Published Date

ISBN 10

7040236079

ISBN 13

9787040236071

Book's by Sam Howison

Practical Applied Mathematics

Practical Applied Mathematics

The Mathematics of Financial Derivatives

The Mathematics of Financial Derivatives

Jump

Jump

Financial Data Analytics with Machine Learning, Optimization and Statistics

Financial Data Analytics with Machine Learning, Optimization and Statistics

Practical Applied Mathematics

Practical Applied Mathematics

The Mathematics of Financial Derivatives

The Mathematics of Financial Derivatives

实用数学

实用数学

Username

Password

Username

Password

Password again

Birthday

Email