Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into

Parametric models with coverage of

Concept of maximum likelihood estimate (MLE) of a probability distribution parameter

MLE of the survival function

Common probability distributions and their analysis

Analysis of exponential distribution as a survival function

Analysis of Weibull distribution as a survival function

Derivation of Gumbel distribution as a survival function from Weibull

Non-parametric models including

Kaplan–Meier (KM) estimator, a derivation of expression using MLE

Fitting KM estimator with an example dataset, Python code and plotting curves

Greenwood’s formula and its derivation

Models with covariates explaining

The concept of time shift and the accelerated failure time (AFT) model

Weibull-AFT model and derivation of parameters by MLE

Proportional Hazard (PH) model

Cox-PH model and Breslow’s method

Significance of covariates

Selection of covariates

The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into

Parametric models with coverage of

Concept of maximum likelihood estimate (MLE) of a probability distribution parameter

MLE of the survival function

Common probability distributions and their analysis

Analysis of exponential distribution as a survival function

Analysis of Weibull distribution as a survival function

Derivation of Gumbel distribution as a survival function from Weibull

Non-parametric models including

Kaplan–Meier (KM) estimator, a derivation of expression using MLE

Fitting KM estimator with an example dataset, Python code and plotting curves

Greenwood’s formula and its derivation

Models with covariates explaining

The concept of time shift and the accelerated failure time (AFT) model

Weibull-AFT model and derivation of parameters by MLE

Proportional Hazard (PH) model

Cox-PH model and Breslow’s method

Significance of covariates

Selection of covariates

The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan–Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood’s formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow’s method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan–Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood’s formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow’s method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.Provides a detailed theoretical foundation of machine learning and computational intelligence algorithmsHighlights the state of art machine learning-based solutions for communication networksPresents video demonstrations and code snippets on each chapter for easy understanding of the conceptsDiscusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networksDemonstrates applications of machine learning techniques for optical networksThe text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.Provides a detailed theoretical foundation of machine learning and computational intelligence algorithmsHighlights the state of art machine learning-based solutions for communication networksPresents video demonstrations and code snippets on each chapter for easy understanding of the conceptsDiscusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networksDemonstrates applications of machine learning techniques for optical networksThe text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.

Provides a detailed theoretical foundation of machine learning and computational intelligence algorithms Highlights the state of art machine learning-based solutions for communication networks Presents video demonstrations and code snippets on each chapter for easy understanding of the concepts Discusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networks Demonstrates applications of machine learning techniques for optical networks

The text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.

Provides a detailed theoretical foundation of machine learning and computational intelligence algorithms Highlights the state of art machine learning-based solutions for communication networks Presents video demonstrations and code snippets on each chapter for easy understanding of the concepts Discusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networks Demonstrates applications of machine learning techniques for optical networks

The text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

Digital twin (DT) technology is a real-time evolving digital duplicate of a physical object or process that contains all its history. It is enabled by massive real-time multi-source data collection and analysis. While 6G is considered as an enabler of digital twins, DT can also be a facilitator for integrating AI and 6G towards reliable, pervasive and efficient intelligent technologies.While the DT concept is familiar among aerospace and industrial engineers, it is a relatively new topic among electronic, electrical, computer, communications and networking engineers. For future massive-scale industrial internet-of-things (IoT) applications facilitated by DTs, a 6G network will be much more advantageous than its 5G counterpart.Digital Twins for 6G: Fundamental theory, technology and applications aims to bring together knowledge from industrial practitioners and researchers, and to introduce novel concepts that can help address the challenges associated with this interdisciplinary topic. The authors will cover fundamentals, enabling technologies, standards and advanced topics of DT and 6G to demystify the DT concept and its networking requirements and benefits, support a broader understanding of DT and its relationship with 6G to a larger audience, support learning and understanding for researchers and professionals working on 5G and 6G, and create a foundation on DT and 6G for the international research community.This book is intended to be both a tutorial of the important topics around digital twin and advanced wireless communications technologies, including 6G, as well as an advanced overview for technical professionals in the communications industry, technical managers, and researchers in both academia and industry.

Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python.The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. You’ll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You’ll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE). Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.What You Will LearnUnderstand applied probability and statistics with financeDesign forecasting models of the stock price with the stochastic process, Monte-Carlo simulation.Option price estimation with both risk-neutral probabilistic and PDE-driven approach.Use Object-oriented Python to design financial models with reusability.Who This Book Is For Data scientists, quantitative researchers and practitioners, software engineers and AI architects interested in quantitative finance

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into

Parametric models with coverage of

Concept of maximum likelihood estimate (MLE) of a probability distribution parameter

MLE of the survival function

Common probability distributions and their analysis

Analysis of exponential distribution as a survival function

Analysis of Weibull distribution as a survival function

Derivation of Gumbel distribution as a survival function from Weibull

Non-parametric models including

Kaplan–Meier (KM) estimator, a derivation of expression using MLE

Fitting KM estimator with an example dataset, Python code and plotting curves

Greenwood’s formula and its derivation

Models with covariates explaining

The concept of time shift and the accelerated failure time (AFT) model

Weibull-AFT model and derivation of parameters by MLE

Proportional Hazard (PH) model

Cox-PH model and Breslow’s method

Significance of covariates

Selection of covariates

The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into

Parametric models with coverage of

Concept of maximum likelihood estimate (MLE) of a probability distribution parameter

MLE of the survival function

Common probability distributions and their analysis

Analysis of exponential distribution as a survival function

Analysis of Weibull distribution as a survival function

Derivation of Gumbel distribution as a survival function from Weibull

Non-parametric models including

Kaplan–Meier (KM) estimator, a derivation of expression using MLE

Fitting KM estimator with an example dataset, Python code and plotting curves

Greenwood’s formula and its derivation

Models with covariates explaining

The concept of time shift and the accelerated failure time (AFT) model

Weibull-AFT model and derivation of parameters by MLE

Proportional Hazard (PH) model

Cox-PH model and Breslow’s method

Significance of covariates

Selection of covariates

The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan–Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood’s formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow’s method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan–Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood’s formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow’s method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.Provides a detailed theoretical foundation of machine learning and computational intelligence algorithmsHighlights the state of art machine learning-based solutions for communication networksPresents video demonstrations and code snippets on each chapter for easy understanding of the conceptsDiscusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networksDemonstrates applications of machine learning techniques for optical networksThe text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.Provides a detailed theoretical foundation of machine learning and computational intelligence algorithmsHighlights the state of art machine learning-based solutions for communication networksPresents video demonstrations and code snippets on each chapter for easy understanding of the conceptsDiscusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networksDemonstrates applications of machine learning techniques for optical networksThe text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.

Provides a detailed theoretical foundation of machine learning and computational intelligence algorithms Highlights the state of art machine learning-based solutions for communication networks Presents video demonstrations and code snippets on each chapter for easy understanding of the concepts Discusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networks Demonstrates applications of machine learning techniques for optical networks

The text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

With the advent of Big Data, conventional communication networks are often limited in their inability to handle complex and voluminous data and information as far as effective processing, transmission, and reception are concerned. This book discusses the evolution of computational intelligence techniques in handling intelligent communication networks.

Provides a detailed theoretical foundation of machine learning and computational intelligence algorithms Highlights the state of art machine learning-based solutions for communication networks Presents video demonstrations and code snippets on each chapter for easy understanding of the concepts Discusses applications including resource allocation, spectrum management, channel estimation, and physical layer of wireless networks Demonstrates applications of machine learning techniques for optical networks

The text is primarily intended for senior undergraduate and graduate students and academic researchers in fields of electrical engineering, electronics and communication engineering, and computer engineering.

Digital twin (DT) technology is a real-time evolving digital duplicate of a physical object or process that contains all its history. It is enabled by massive real-time multi-source data collection and analysis. While 6G is considered as an enabler of digital twins, DT can also be a facilitator for integrating AI and 6G towards reliable, pervasive and efficient intelligent technologies.While the DT concept is familiar among aerospace and industrial engineers, it is a relatively new topic among electronic, electrical, computer, communications and networking engineers. For future massive-scale industrial internet-of-things (IoT) applications facilitated by DTs, a 6G network will be much more advantageous than its 5G counterpart.Digital Twins for 6G: Fundamental theory, technology and applications aims to bring together knowledge from industrial practitioners and researchers, and to introduce novel concepts that can help address the challenges associated with this interdisciplinary topic. The authors will cover fundamentals, enabling technologies, standards and advanced topics of DT and 6G to demystify the DT concept and its networking requirements and benefits, support a broader understanding of DT and its relationship with 6G to a larger audience, support learning and understanding for researchers and professionals working on 5G and 6G, and create a foundation on DT and 6G for the international research community.This book is intended to be both a tutorial of the important topics around digital twin and advanced wireless communications technologies, including 6G, as well as an advanced overview for technical professionals in the communications industry, technical managers, and researchers in both academia and industry.

Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python.The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. You’ll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You’ll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE). Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.What You Will LearnUnderstand applied probability and statistics with financeDesign forecasting models of the stock price with the stochastic process, Monte-Carlo simulation.Option price estimation with both risk-neutral probabilistic and PDE-driven approach.Use Object-oriented Python to design financial models with reusability.Who This Book Is For Data scientists, quantitative researchers and practitioners, software engineers and AI architects interested in quantitative finance