Tabla de Contenidos: “…Front Cover; Statistical Computing in Nuclear Imaging; Series in Medical Physics and Biomedical Engineering; Dedication; Contents; List of Figures; List of Tables; About the Series; Preface; About the Author; Chapter 1 Basic statistical concepts; Chapter 2 Elements of decision theory; Chapter 3 Counting statistics; Chapter 4 Monte Carlo methods in posterior analysis; Chapter 5 Basics of nuclear imaging; Chapter 6 Statistical computing; Appendix A Probability distributions; Appendix B Elements of set theory; Appendix C Multinomial distribution of singlevoxel…”

“…The method was applied to a detector system at the In Vivo Measurement Laboratory at Karlsruhe Institute of Technology using Monte Carlo simulation and computational phantoms…”

Tabla de Contenidos: “…6.3 Scale and Resolution6.4 The Dilation Equation and the Haar Transform; 6.5 Decomposition and Reconstruction; 6.6 Compression; 6.7 Coefficient Conditions; 6.8 Multiresolution Analysis; 6.9 Wavelets in the Fourier Domain; 6.10 Two-Dimensional Wavelets; 6.11 Further Reading; 6.12 Exercises; Chapter 7. Monte Carlo Integration; 7.1 Introduction; 7.2 Basic Monte Carlo Ideas; 7.3 Confidence; 7.4 Blind Monte Carlo; 7.5 Informed Monte Carlo; 7.6 Adaptive Sampling; 7.7 Other Approaches; 7.8 Summary; 7.9 Further Reading; 7.10 Exercises; Chapter 8. …”

Tabla de Contenidos: “…Front Cover; Dedication; Contents; Preface; List of Figures; List of Tables; Chapter 1: Option Pricing in a Nutshell; Chapter 2: Monte Carlo; Chapter 3: Some Excursions in Option Pricing; Chapter 4: Nonlinear PDEs: A Bit of Theory; Chapter 5: Examples of Nonlinear Problems in Finance; Chapter 6: Early Exercise Problems; Chapter 7: Backward Stochastic Differential Equations; Chapter 8: The Uncertain Lapse and Mortality Model; Chapter 9: The Uncertain Volatility Model; Chapter 10: McKean Nonlinear Stochastic Differential Equations…”

Tabla de Contenidos: “…Unsupervised learning -- Chapter 15. Markov Chain Monte Carlo (MCMC) methods -- Chapter 16. Graphical models -- Chapter 17. …”

Tabla de Contenidos: “…Part I: Foundation -- Chapter 1: Introduction to Reinforcement Learning -- Chapter 2: Markov Decision Processes -- Chapter 3: Dynamic Programming -- Chapter 4: Monte Carlo Methods -- Chapter 5: Temporal Difference Learning -- Part II: Value Function Approximation -- Chapter 6: Linear Value Function Approximation -- Chapter 7: Nonlinear Value Function Approximation -- Chapter 8: Improvement to DQN -- Part III: Policy Approximation -- Chapter 9: Policy Gradient Methods -- Chapter 10: Problems with Continuous Action Space -- Chapter 11: Advanced Policy Gradient Methods -- Part IV: Advanced Topics -- Chapter 12: Distributed Reinforcement Learning -- Chapter 13: Curiosity-Driven Exploration -- Chapter 14: Planning with a Model – AlphaZero…”

Tabla de Contenidos: “…Chapter 1: Options Concept -- Chapter 2: Financial Derivatives -- Chapter 3: Basic Algorithms -- Chapter 4: Object-Oriented Techniques -- Chapter 5: Design Patterns for Options Processing -- Chapter 6: C++ Template-Based Techniques -- Chapter 7: STL for Derivative Programming -- Chapter 8: Functional Programming Techniques -- Chapter 9: Linear Algebra Algorithms -- Chapter 10: Numerical Analysis Algorithms in C++ -- Chapter 11: Solving Models Based on Differential Equations -- Chapter 12: Basic Models for Options Pricing -- Chapter 13: Monte-Carlo Techniques for Options Pricing -- Chapter 14: Back Testing Option Strategies -- Chapter 15: Using C++ libraries for Finance -- Chapter 16: Credit Derivatives -- Chapter 17: Processing Financial Data…”

Tabla de Contenidos: “…Linear Systems Response, State-Space Modeling, and Monte Carlo Simulation -- PART 2. KALMAN FILTERING AND APPLICATIONS Chapter 4. …”

Tabla de Contenidos:

Tabla de Contenidos: “…SVD applied on handwritten digits using scikit-learn -- Deep auto encoders -- Model building technique using encoder-decoder architecture -- Deep auto encoders applied on handwritten digits using Keras -- Summary -- Chapter 9: Reinforcement Learning -- Introduction to reinforcement learning -- Comparing supervised, unsupervised, and reinforcement learning in detail -- Characteristics of reinforcement learning -- Reinforcement learning basics -- Category 1 - value based -- Category 2 - policy based -- Category 3 - actor-critic -- Category 4 - model-free -- Category 5 - model-based -- Fundamental categories in sequential decision making -- Markov decision processes and Bellman equations -- Dynamic programming -- Algorithms to compute optimal policy using dynamic programming -- Grid world example using value and policy iteration algorithms with basic Python -- Monte Carlo methods -- Comparison between dynamic programming and Monte Carlo methods -- Key advantages of MC over DP methods -- Monte Carlo prediction -- The suitability of Monte Carlo prediction on grid-world problems -- Modeling Blackjack example of Monte Carlo methods using Python -- Temporal difference learning -- Comparison between Monte Carlo methods and temporal difference learning -- TD prediction -- Driving office example for TD learning -- SARSA on-policy TD control -- Q-learning - off-policy TD control -- Cliff walking example of on-policy and off-policy of TD control -- Applications of reinforcement learning with integration of machine learning and deep learning -- Automotive vehicle control - self-driving cars -- Google DeepMind's AlphaGo -- Robo soccer -- Further reading -- Summary -- Index…”

Tabla de Contenidos: “…Fundamentals of Detection Theory; 12. Monte Carlo Methods for Statistical Signal Processing; 13. …”

Tabla de Contenidos: “…6.6.1 Double Barrier Options and Dirichlet Boundary Conditions -- 6.6.2 Artificial Boundary Conditions and the Neumann Case -- 7 Finite Element Methods -- 7.1 Introduction -- 7.1.1 Weighted Residual Methods -- 7.1.2 Basic Steps -- 7.2 Grid Generation -- 7.3 Elements -- 7.3.1 1D Elements -- 7.3.2 2D Elements -- 7.4 The Assembling Process -- 7.4.1 Element Matrices -- 7.4.2 Time Discretization -- 7.4.3 Global Matrices -- 7.4.4 Boundary Conditions -- 7.4.5 Application of the Finite Element Method to Convection-Diffusion-Reaction Problems -- 7.5 A Zero Coupon Bond Under the Two Factor Hull-White Model -- 7.6 Appendix: Higher Order Elements -- 7.6.1 3D Elements -- 7.6.2 Local and Natural Coordinates -- 8 Solving Systems of Linear Equations -- 8.1 Direct Methods -- 8.1.1 Gaussian Elimination -- 8.1.2 Thomas Algorithm -- 8.1.3 LU Decomposition -- 8.1.4 Cholesky Decomposition -- 8.2 Iterative Solvers -- 8.2.1 Matrix Decomposition -- 8.2.2 Krylov Methods -- 8.2.3 Multigrid Solvers -- 8.2.4 Preconditioning -- 9 Monte Carlo Simulation -- 9.1 The Principles of Monte Carlo Integration -- 9.2 Pricing Derivatives with Monte Carlo Methods -- 9.2.1 Discretizing the Stochastic Differential Equation -- 9.2.2 Pricing Formalism -- 9.2.3 Valuation of a Steepener under a Two Factor Hull-White Model -- 9.3 An Introduction to the Libor Market Model -- 9.4 Random Number Generation -- 9.4.1 Properties of a Random Number Generator -- 9.4.2 Uniform Variates -- 9.4.3 Random Vectors -- 9.4.4 Recent Developments in Random Number Generation -- 9.4.5 Transforming Variables -- 9.4.6 Random Number Generation for Commonly Used Distributions -- 10 Advanced Monte Carlo Techniques -- 10.1 Variance Reduction Techniques -- 10.1.1 Antithetic Variates -- 10.1.2 Control Variates -- 10.1.3 Conditioning -- 10.1.4 Additional Techniques for Variance Reduction -- 10.2 Quasi Monte Carlo Method…”

“…A key advantage of this model is given by the ability to generate multi-mode predictions in a single inference step, thus avoiding the need for Monte Carlo simulation…”

Tabla de Contenidos: “…Front Cover; Contents; Preface; Acknowledgements; Author; Chapter 1: Analytics Background and Architectures; Chapter 2: Mathematical and Statistical Preliminaries; Chapter 3: Statistics for Descriptive Analytics; Chapter 4: Bayesian Probability and Inference; Chapter 5: Inferential Statistics and Predictive Analytics; Chapter 6: Artificial Intelligence for Symbolic Analytics; Chapter 7: Probabilistic Graphical Modeling; Chapter 8: Decision Support and Prescriptive Analytics; Chapter 9: Time Series Modeling and Forecasting; Chapter 10: Monte Carlo Simulation…”

Tabla de Contenidos: “…Chapter 8: Modeling and Simulation of Single-Event Effects in Devices and CircuitsChapter 9: Soft-Error Rate (SER) Monte Carlo Simulation Codes; Chapter 10: Scaling Effects and Their Implications for Soft Errors; Chapter 11: Natural Radiation in Nonvolatile Memories: A Case Study; Chapter 12: SOI, FinFET, and Emerging Devices; Back Cover…”

Tabla de Contenidos: “…Variance Reduction; 5.1. Monte-Carlo Integration; 5.2. Importance Sampling; 5.3. …”

Tabla de Contenidos: “…Stochastic Volatility and Realized Stochastic Volatility Models; 22. Monte Carlo Methods and Zero Variance Principle; 23. …”

Tabla de Contenidos: “…-- An inventory problem in Monte Carlo simulation -- Monte Carlo simulation in basketball -- The volatility plot -- Implied volatilities -- The portfolio valuation…”

Tabla de Contenidos: “…Validation methods for time series -- Feature engineering for time series -- Time series forecasting as reduced regression -- Forecasting with Meta's Prophet -- AutoML for time series forecasting with PyCaret -- Summary -- Chapter 8: Multi-Factor Models -- Estimating the CAPM -- Estimating the Fama-French three-factor model -- Estimating the rolling three-factor model on a portfolio of assets -- Estimating the four- and five-factor models -- Estimating cross-sectional factor models using the Fama-MacBeth regression -- Summary -- Chapter 9: Modeling Volatility with GARCH Class Models -- Modeling stock returns' volatility with ARCH models -- Modeling stock returns' volatility with GARCH models -- Forecasting volatility using GARCH models -- Multivariate volatility forecasting with the CCC-GARCH model -- Forecasting the conditional covariance matrix using DCC-GARCH -- Summary -- Chapter 10: Monte Carlo Simulations in Finance -- Simulating stock price dynamics using a geometric Brownian motion -- Pricing European options using simulations -- Pricing American options with Least Squares Monte Carlo -- Pricing American options using QuantLib -- Pricing barrier options -- Estimating Value-at-Risk using Monte Carlo -- Summary -- Chapter 11: Asset Allocation -- Evaluating an equally-weighted portfolio's performance -- Finding the efficient frontier using Monte Carlo simulations -- Finding the efficient frontier using optimization with SciPy -- Finding the efficient frontier using convex optimization with CVXPY -- Finding the optimal portfolio with Hierarchical Risk Parity -- Summary -- Chapter 12: Backtesting Trading Strategies -- Vectorized backtesting with pandas -- Event-driven backtesting with backtrader -- Backtesting a long/short strategy based on the RSI -- Backtesting a buy/sell strategy based on Bollinger bands…”