The real world is perceived and broken down as data, models and algorithms in the eyes of physicists and engineers. Data is noisy by nature and classical statistical tools have so far been successful in dealing with relatively smaller levels of randomness. The recent emergence of Big Data and the required computing power to analyse them have rendered classical tools outdated and insufficient. Tools such as random matrix theory and the study of large sample covariance matrices can efficiently process these big data sets and help make sense of modern, deep learning algorithms. Presenting an introductory calculus course for random matrices, the book focusses on modern concepts in matrix theory, generalising the standard concept of probabilistic independence to non-commuting random variables. Concretely worked out examples and applications to financial engineering and portfolio construction make this unique book an essential tool for physicists, engineers, data analysts, and economists.
"Sinopsis" puede pertenecer a otra edición de este libro.
Marc Potters is Chief Investment Officer of CFM, an investment firm based in Paris. Marc maintains strong links with academia and as an expert in Random Matrix Theory, he has taught at UCLA and Sorbonne University. He is co-author of Theory of Financial Risk and Derivative Pricing (Cambridge 2003).
Jean-Philippe Bouchaud is a pioneer in Econophysics. His research includes random matrix theory, statistics of price formation, stock market fluctuations, and agent-based models for financial markets and macroeconomics. His previous books include Theory of Financial Risk and Derivative Pricing (Cambridge, 2003) and Trades, Quotes & Prices (Cambridge, 2018), and he has been the recipient of several prestigious, international awards.
"Sobre este título" puede pertenecer a otra edición de este libro.
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Condición: New. Nº de ref. del artículo: ABLIING23Mar2317530285393
Cantidad disponible: Más de 20 disponibles
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Condición: New. Nº de ref. del artículo: 41839287-n
Cantidad disponible: Más de 20 disponibles
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: GB-9781108488082
Cantidad disponible: 2 disponibles
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: GB-9781108488082
Cantidad disponible: 2 disponibles
Librería: California Books, Miami, FL, Estados Unidos de America
Condición: New. Nº de ref. del artículo: I-9781108488082
Cantidad disponible: Más de 20 disponibles
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 41839287
Cantidad disponible: Más de 20 disponibles
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
Hardcover. Condición: new. Hardcover. The real world is perceived and broken down as data, models and algorithms in the eyes of physicists and engineers. Data is noisy by nature and classical statistical tools have so far been successful in dealing with relatively smaller levels of randomness. The recent emergence of Big Data and the required computing power to analyse them have rendered classical tools outdated and insufficient. Tools such as random matrix theory and the study of large sample covariance matrices can efficiently process these big data sets and help make sense of modern, deep learning algorithms. Presenting an introductory calculus course for random matrices, the book focusses on modern concepts in matrix theory, generalising the standard concept of probabilistic independence to non-commuting random variables. Concretely worked out examples and applications to financial engineering and portfolio construction make this unique book an essential tool for physicists, engineers, data analysts, and economists. Classical statistical tools that handled real-life data have become inadequate upon the emergence of Big Data. Random matrix theory and free calculus introduced here present valuable solutions to the complex challenges posed by large datasets. Real world applications make it an essential tool for physicists, engineers, data analysts and economists. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781108488082
Cantidad disponible: 1 disponibles
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Condición: New. Nº de ref. del artículo: 41839287-n
Cantidad disponible: Más de 20 disponibles
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Condición: New. In. Nº de ref. del artículo: ria9781108488082_new
Cantidad disponible: 2 disponibles
Librería: Books Puddle, New York, NY, Estados Unidos de America
Condición: New. Nº de ref. del artículo: 26384140660
Cantidad disponible: 1 disponibles