Tabla de Contenidos: “…Response PlanningReporting; Implementation; Review; Post-Project Review; Conclusion; Chapter 15: Simulating Possible Futures (Quantitative Analysis); Introducing Monte Carlo Analysis; Quantitative Risk Analysis in the Project Life Cycle; Quantitative Risk Analysis in the ATOM Process; Getting Started; Analysis; Interpreting Outputs; Using Results; Conclusion; Chapter 16: Managing Risk in Programs; From Projects to Programs; Program Management Basics; Aim and Scope of Program Risk Management; Managing Program Risk; Epilogue: Next Steps; Step 1: Appoint an Organizational Risk Sponsor…”

Tabla de Contenidos: “…Node Parallelism for Graph Centrality Metrics 3 Optimizing parallel prefix operations for the Fermi architecture 4 Building an Efficient Hash Table on the GPU 5 An Efficient CUDA Algorithm for the Maximum Network Flow Problem 6 On Improved Memory Access Patterns for Cellular Automata Using CUDA 7 Fast Minimum Spanning Tree Computation on Large Graphs 8 Fast in-place sorting with CUDA based on bitonic sort Part 2: Numerical Algorithms - Frank Jargstorff, NVIDIA 9 Interval Arithmetic in CUDA 10 Approximating the erfinv Function 11 A Hybrid Method for Solving Tridiagonal Systems on the GPU 12 LU Decomposition in CULA 13 GPU Accelerated Derivative-free Optimization Part 3: Engineering Simulation - Peng Wang, NVIDIA 14 Large-scale gas turbine simulations on GPU clusters 15 GPU acceleration of rarefied gas dynamic simulations 16 Assembly of Finite Element Methods on Graphics Processors 17 CUDA implementation of Vertex-Centered, Finite Volume CFD methods on Unstructured Grids with Flow Control Applications 18 Solving Wave Equations on Unstructured Geometries 19 Fast electromagnetic integral equation solvers on graphics processing units (GPUs) Part 4: Interactive Physics for Games and Engineering Simulation - Richard Tonge, NVIDIA 20 Solving Large Multi-Body Dynamics Problems on the GPU 21 Implicit FEM Solver in CUDA 22 Real-time Adaptive GPU multi-agent path planning Part 5: Computational Finance - Thomas Bradley, NVIDIA 23 High performance finite difference PDE solvers on GPUs for financial option pricing 24 Identifying and Mitigating Credit Risk using Large-scale Economic Capital Simulations 25 Financial Market Value-at-Risk Estimation using the Monte Carlo Method Part 6: Programming Tools and Techniques - Cliff Wooley, NVIDIA 26 Thrust: A Productivity-Oriented Library for CUDA 27 GPU Scripting and Code Generation with PyCUDA 28 Jacket: GPU Powered MATLAB Acceleration 29 Accelerating Development and Execution Speed with Just In Time GPU Code Generation 30 GPU Application Development, Debugging, and Performance Tuning with GPU Ocelot 31 Abstraction for AoS and SoA Layout in C++ 32 Processing Device Arrays with C++ Metaprogramming 33 GPU Metaprogramming: A Case Study in Biologically-Inspired Machine Vision 34 A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs 35 Dynamic Load Balancing using Work-Stealing 36 Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads…”

Tabla de Contenidos: “…MILLIONS OF NUMBERS WITHOUT SOUL -- NUMERICAL SIMULATION OF GROUNDWATER CIRCULATION WELLS: A STOCHASTIC APPROACH TO EVALUATE THEIR EFFECTIVENESS -- COMPOSITIONAL FINGERPRINTING TOOLS FOR HYDROCARBON CONTAMINATED SITE CHARACTERIZATION: SOURCE APPORTIONMENT AND SPILL DATING APPLICATIONS AT TWO FIELD CASE STUDIES -- MULTI-ISOTOPIC APPROACH FOR THE GROUNDWATER FLOW AND CONTAMINANTS CHARACTERIZATION IN A COMPLEX CONTAMINATED SITE: EX-ACNA DI CENGIO (SV) -- AN INTERDISCIPLINARY APPROACH TO EVALUATE THE NATURAL ATTENUATION AS POTENTIAL REMEDIATION ACTION -- INNOVATIVE TOOLS FOR THE ANALYSIS OF BIOLOGICAL PROCESSES FOR THE REMEDIATION OF CONTAMINATED AQUIFERS -- INTEGRATED USE OF BIODEGRADABLE SURFACTANTS AND ISCO REAGENTS FOR THE TREATMENT OF HYDROCARBON CONTAMINATED SOILS AND AQUIFERSHYDROGEOLOGICAL CHARACTERIZATION OF A CLOSED MSW LANDFILL USING MULTIVARIATE STATISTICAL ANALYSIS AND GROUNDWATER NUMERICAL MODELLING -- URBAN HYDROGEOLOGY -- HOW MODELS SURPRISE -- NULLSPACE MONTE CARLO PARTICLE TRACKING TO IDENTIFY SOURCE AREAS OF GROUNDWATER POLLUTION IN THE N-W MILANO FUA (AMIIGA PROJECT-CE No 32) -- WATER RESOURCES PROVIDED BY THE MAJOR AQUIFERS IN THE PROVINCE OF SIENA WITHIN THE FRAMEWORK OF THE PROVINCIAL TERRITORIAL PLAN (PTCP) -- HYDROGEOLOGICAL SETTING OF A ROME CITY SECTOR: SHALLOW GROUNDWATER IN THE RIGHT BANK OF TIBER RIVER INSIDE THE G.R.A HIGHWAY -- A PRELIMINARY METHOD FOR THE ASSESSMENT OF GROUNDWATER FLOODING SUSCEPTIBILITY IN URBAN AND RURAL AREAS -- HYDROTHERMAL MANAGEMENT OF AN ATES SYSTEM IN WALLONIA, BELGIUM -- EVALUATION OF HYDROFACIES CONNECTIVITY AND HYDRAULIC PROPERTIES BY USING FACIES AND SEQUENCE STRATIGRAPHY ANALYSIS: THE CASE OF THE PALATINE HILL, CENTRAL ARCHAEOLOGICAL AREA OF ROME -- EVALUATION OF NITRATE TRENDS IN GROUNDWATER: A CASE STUDY IN A GROUNDWATER BODY IN SOUTHERN ITALY -- PRESERVING ARCHAEOLOGICAL HERITAGES FROM FLOOD HAZARD: USE OF 3D GEOLOGICAL MODELS AS A BASE FOR CONCEPTUAL HYDROGEOLOGICAL SCHEME IN THE AREA OF THE COLISEUM (ROME) -- FOLLOW-UP INVESTIGATION ON GROUNDWATER FLOWPATH AT REGIONAL SCALE USING A MULTI-ISOTOPIC APPROACH: THE CASE STUDY OF LOMBARDY REGION -- ASSESSING AQUIFER VULNERABILITY TO SEAWATER INTRUSION ACCOUNTING FOR SURFACE WATER BODIES -- EVALUATION OF THE DEPOSITION, INFILTRATION AND DRAINAGE OF THE ATMOSPHERIC POLLUTANTS IN THE VADOSE ZONE -- HOW LONG WILL IT TAKE? …”

Tabla de Contenidos: “…Lillie -Total Gian Resonance Photonuclear Cross-Sections for Light Nuclei: A Database for the FLUKA Monte Carlo Transport Mode by A. Fasso, A. Ferrari, and P.R. …”

Tabla de Contenidos: “…. -- Einleitung -- I Studien zur Umweltbewertung -- 1 Umweltbewertung und CVM 11 -- 1.1 Einführung zur Umweltbewertung -- 1.2 Theoretische Grundlagen der CVM -- 1.3 Contingent Valuation - die Praxis -- 1.3.1 Definition des Umweltguts -- 1.3.2 Der hypothetische Markt -- 1.3.3 Die Frage nach der Zahlungsbereitschaft -- 1.3.4 Soziodemographische und psychosoziale Daten -- 1.3.5 CVM-Interview bei Umweltschäden -- 2 Das statistische Modell -- 2.1 Ökonomische Fundierung -- 2.2 Klassische parametrische Modelle -- 2.2.1 Die Parameterschätzung -- 2.2.2 Zur Modellwahl -- 2.3 Weitere Ansätze der klassischen Statistik -- 2.4 Ein alternativer Ansatz: Das Bayes'sche Modell -- 2.4.1 Bayes'sches Modell für CVM Daten -- 2.4.2 Der Gibbs-Sampler -- 2.4.3 Über Konvergenz und Konfidenz -- 2.4.4 Vorteile des Bayes'schen Ansatzes -- 2.4.5 Die Wahl der A-Priori-Verteilung -- 3 Zahlungsbereitschaftsanalyse -- 3.1 Eine Monte Carlo Simulation -- 3.1.1 Simulation einer CVM-Befragung -- 3.1.2 Klassische Modellierung . -- 3.1.3 Bayes'sche Modellierung -- 3.2 Bewertung des Cottbuser Ostsees . -- 3.2.1 Ein neuer See in der Lausitz -- 3.2.2 Ergebnisse der Befragung . -- 3.2.3 Berechnung der Zahlungsbereitschaft -- 4 Zwischenfazit -- II N utzentransfer -- 5 Klassischer N utzentransfer -- 5.1 Nutzentransfer und Umweltbewertung -- 5.2 Herkömmliche Verfahren -- 5.2.1 Value Transfer -- 5.2.2 Benefit Function Transfer -- 5.3 Validitätstests für den Nutzentransfer -- 5.4 Die Grenzen klassischer Verfahren -- 6 Bayes'scher Nutzentransfer -- 6.1 Bayes'sche Verfahren als Chance für den NT -- 6.2 Anwendung auf eine simulierte Befragung -- 6.3 Bayes'scher NT für Dichotomous Choice Daten -- 6.3.1 Einfacher Bayes-Transfer -- 6.3.2 Der Power Prior -- 6.4 Bayes'scher NT für den Cottbuser Ostsee -- 6.5 Ein neuer Test für den Nutzentransfer -- 7 Zusammenfassung und Ausblick -- 2.1 Parametrische Modellierung von CVM Daten -- 2.2 Graphische Analyse der Konvergenz von Markov-Ketten -- 2.3 Veränderung des Konfidenzintervalls -- 3.1 Nichtparametrische Analyse der Erstantworten -- 3.2 Häufigkeiten aller Antworten -- 3.3 Graphische Analyse -- 3.4 Textauszug der Szenariodarstellung im Fragebogen -- 6.1 A-Posteriori-Verteilung im Vergleich…”

Tabla de Contenidos: “…Generator outputs of StackedGAN -- Conclusion -- Reference -- Chapter 7: Cross-Domain GANs -- Principles of CycleGAN -- The CycleGAN Model -- Implementing CycleGAN using Keras -- Generator outputs of CycleGAN -- CycleGAN on MNIST and SVHN datasets -- Conclusion -- References -- Chapter 8: Variational Autoencoders (VAEs) -- Principles of VAEs -- Variational inference -- Core equation -- Optimization -- Reparameterization trick -- Decoder testing -- VAEs in Keras -- Using CNNs for VAEs -- Conditional VAE (CVAE) -- -VAE: VAE with disentangled latent representations -- Conclusion -- References -- Chapter 9: Deep Reinforcement Learning -- Principles of reinforcement learning (RL) -- The Q value -- Q-Learning example -- Q-Learning in Python -- Nondeterministic environment -- Temporal-difference learning -- Q-Learning on OpenAI gym -- Deep Q-Network (DQN) -- DQN on Keras -- Double Q-Learning (DDQN) -- Conclusion -- References -- Chapter 10: Policy Gradient Methods -- Policy gradient theorem -- Monte Carlo policy gradient (REINFORCE) method -- REINFORCE with baseline method -- Actor-Critic method -- Advantage Actor-Critic (A2C) method -- Policy Gradient methods with Keras -- Performance evaluation of policy gradient methods -- Conclusion -- References -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Setting up PyPy -- Running a particle simulator in PyPy -- Other interesting projects -- Summary -- Chapter 6: Implementing Concurrency -- Asynchronous programming -- Waiting for I/O -- Concurrency -- Callbacks -- Futures -- Event loops -- The asyncio framework -- Coroutines -- Converting blocking code into non-blocking code -- Reactive programming -- Observables -- Useful operators -- Hot and cold observables -- Building a CPU monitor -- Summary -- Chapter 7: Parallel Processing -- Introduction to parallel programming -- Graphic processing units -- Using multiple processes -- The Process and Pool classes -- The Executor interface -- Monte Carlo approximation of pi -- Synchronization and locks -- Parallel Cython with OpenMP -- Automatic parallelism -- Getting started with Theano -- Profiling Theano -- Tensorflow -- Running code on a GPU -- Summary -- Chapter 8: Distributed Processing -- Introduction to distributed computing -- An introduction to MapReduce -- Dask -- Directed Acyclic Graphs -- Dask arrays -- Dask Bag and DataFrame -- Dask distributed -- Manual cluster setup -- Using PySpark -- Setting up Spark and PySpark -- Spark architecture -- Resilient Distributed Datasets -- Spark DataFrame -- Scientific computing with mpi4py -- Summary -- Chapter 9: Designing for High Performance -- Choosing a suitable strategy -- Generic applications -- Numerical code -- Big data -- Organizing your source code -- Isolation, virtual environments, and containers -- Using conda environments -- Virtualization and Containers -- Creating docker images -- Continuous integration -- Summary -- Index…”

Tabla de Contenidos: “…Environmental ImpactHumidity; Conductivity; Temperature; Model and simulation; Model Geometries; Stripline structures; Microstrip structures; Corner Models; Iterative corner model; Monte Carlo corner model; Ideal Assumptions: Homogeneous Impedance; Ideal Assumptions: Crosstalk Aggressors; Transmitters; IBIS Models; Spice Voltage Source Model; Linearity test; 3D Modeling; Ports/Terminals; Wave ports; Lumped ports; Model Analysis Settings; Discrete or interpolating solutions; Frequency range and step size; Port order; Normalize result to 50ohms; Plated-Through-Hole Via; Model Techniques…”

Tabla de Contenidos: “…2.8.2 Resolution and error2.8.3 Uncertainty in measurement; 2.8.3.1 The propagation of probability distributions; 2.8.3.2 The GUM uncertainty framework; 2.8.3.3 A Monte Carlo method; 2.9 The laser; 2.9.1 Theory of the helium-neon laser; 2.9.2 Single-mode laser wavelength stabilisation schemes; 2.9.3 Laser frequency stabilisation using saturated absorption; 2.9.3.1 Two-mode stabilisation; 2.9.4 Zeeman-stabilised 633nm lasers; 2.9.5 Frequency calibration of a (stabilised) 633nm laser; 2.9.6 Modern and future laser frequency standards; References…”

Tabla de Contenidos: “…Parallel Simulation on Rayleigh-Bénard Convection in 2D by the Direct Simulation Monte Carlo Method; Chapter 10. Distributed Implementation of KIVA-3 on the Intel Paragon; Chapter 11. …”

Tabla de Contenidos: “…The Resolution Gate and Film Aspect RatioDepth of Field; Lens Artifacts; Motion Blur; Matching Real-World Cameras; Aspect Ratio; Matching the Camera; Summary; Chapter 8: Lighting and Rendering; Characteristics of Light and Shadow; Light; Shadow; Photometric Lights; Deep Shadows; Global Illumination; Irradiance Caching; Monte Carlo; Environment; Image-Based Lighting; Using sIBL in 401; Lighting Phenomena: Volumetrics and Caustics; Volumetrics; Caustics; Light Linking; Studio Lighting: Reflectors; Render Settings; Antialiasing; Geometry; Ray Tracing; Rendering for Print; Summary…”

Tabla de Contenidos: “…Chapter 50 Using Moving Averages to Understand Time SeriesChapter 51 Forecasting with Moving Averages; Chapter 52 Forecasting in the Presence of Special Events; Chapter 53 An Introduction to Random Variables; Chapter 54 The Binomial and Hypergeometric Random Variables; Chapter 55 The Poisson and Exponential Random Variable; Chapter 56 The Normal Random Variable; Chapter 57 Weibull and Beta Distributions: Modeling Machine Life and Duration of a Project; Chapter 58 Introduction to Monte Carlo Simulation; Chapter 59 Calculating an Optimal Bid…”

Tabla de Contenidos: “…2.5 The Van der Waals Equation of State (EOS)2.6 The Criterion of the Thermal Energy kT for Gauging the Strength of an Interaction; 2.7 Classification of Forces and Pair Potentials; 2.8 Theoretical Analyses of Multimolecular Systems: Continuum and Molecular Approaches; 2.9 Molecular Approaches via Computer Simulations: Monte Carlo (MC) and Molecular Dynamics (MD); 2.10 Newton's Laws Applied to Two-Body Collisions; 2.11 Kinetic and Statistical Aspects of Multiple Collisions: the Boltzmann Distribution; Problems and Discussion Topics…”

Tabla de Contenidos: “…The Nature of Risk and ReturnUncertainty versus Risk; Risk Simulation Applications; Running a Monte Carlo Simulation; Using Forecast Charts and Confidence Intervals; Correlations and Precision Control; Chapter 8 Projections and Risk Assessment; Different Types of Forecasting Techniques; Running the Forecasting Tool in Risk Simulator; Time-Series Analysis; Multivariate Regression; Stochastic Forecasting; Nonlinear Extrapolation; Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) Advanced Time-Series; Auto ARIMA (Box-Jenkins ARIMA Advanced Time-Series); Basic Econometrics…”

Tabla de Contenidos: “…Partie 3: ARBRES BINOMIAUX ET TRINOMIAUX ET SIMULATION MONTE CARLOChapitre 6: VARIATIONS SUR LES ASPECTS THÉORIQUES ET PRATIQUES D'OPTIMISATION; Chapitre 7: DE LA CONSTRUCTION D'ARBRES BINOMIAUX ET TRINOMIAUX DES PRIX DES PRODUITS DÉRIVÉS ET DE SES RAPPORTS AVEC LA SIMULATION MONTE CARLO; Chapitre 8: ARBRES BINOMIAUX ADDITIFS ET MULTIPLICATIFS POUR ÉVALUER LES OPTIONS AMÉRICAINES CLASSIQUES SUR ACTIONS PORTANT DIVIDENDE; Partie 4: QUELQUES APPLICATIONS…”

Tabla de Contenidos: “…Chapter 18: Statistical and Theoretical Concepts -- Discrete and Continuous Returns -- Arithmetic and Geometric Average Returns -- Standard Deviation -- Correlation -- Covariance -- Covariance Invertibility -- Maximum Likelihood Estimation -- Mapping High-Frequency Statistics onto Low-Frequency Statistics -- Portfolios -- Probability Distributions -- The Central Limit Theorem -- The Normal Distribution -- Higher Moments -- The Lognormal Distribution -- Elliptical Distributions -- Probability of Loss -- Value at Risk -- Utility Theory -- Sample Utility Functions -- Alternative Utility Functions -- Expected Utility -- Certainty Equivalents -- Mean-Variance Analysis for More Than Two Assets -- Equivalence of Mean-Variance Analysis and Expected Utility Maximization -- Monte Carlo Simulation -- Bootstrap Simulation -- References -- Note -- Chapter 19: Glossary of Terms -- Index -- EULA…”

Tabla de Contenidos: “…Multitarget Tracking and Classification in Collaborative Sensor Networks via Sequential Monte Carlo (Tom Vercauteren and Xiaodong Wang) -- 14.1 Introduction -- 14.2 System Description and Problem Formulation -- 14.3 Sequential Monte Carlo Methods -- 14.4 Joint Single-Target Tracking and Classification -- 14.5 Multiple-Target Tracking and Classification -- 14.6 Sensor Selection -- 14.7 Simulation Results -- Conclusion -- Appendix: Derviations of (14.38 and (14.40) -- References -- 15. …”

Tabla de Contenidos: “…7 Valuation of American Options by Simulation7.1 Introduction; 7.2 Financial Model; 7.3 American Option Valuation; 7.3.1 Problem Formulations; 7.3.2 Valuation Algorithms; 7.4 Numerical Results; 7.4.1 American Put Option; 7.4.2 American Short Condor Spread; 7.5 Conclusions; 7.6 Python Scripts; 7.6.1 Binomial Valuation; 7.6.2 Monte Carlo Valuation with LSM; 7.6.3 Primal and Dual LSM Algorithms; PART THREE Market-Based Valuation; 8 A First Example of Market-Based Valuation; 8.1 Introduction; 8.2 Market Model; 8.3 Valuation; 8.4 Calibration; 8.5 Simulation; 8.6 Conclusions; 8.7 Python Scripts…”

Tabla de Contenidos: “…Sistemas con reparaciónModelos de Markov; Redes de Petri; Generalidades ; Paralelismo o concurrencia ; Sincronización ; Secuencialidad ; Conflicto ; Simulación de Monte Carlo; Mantenibilidad y disponibilidad ; Generalidades; Mantembilidad; Disponibilidad; Conclusiones; Fiabilidad de sistemas electrónicos ; Generalidades; Fiabilidad de componentes electrónicos; Modelo de fiabilidad de un componente electrónico; Tasa de fallos de componentes electrónicos; Condiciones de diseño; Fiabilidad de sistemas electrónicos; Fiabilidad del hardware y del software ; El ordenador y sus componentes…”

Tabla de Contenidos: “…How to Build Robust AI for Your Game / section II Architecture -- chapter 8 Modular AI / chapter 9 Overcoming Pitfalls in Behavior Tree Design / chapter 10 From Behavior to Animation -- A Reactive AI Architecture for Networked First-Person Shooter Games / chapter 11 A Character Decision-Making System for FINAL FANTASY XV by Combining Behavior Trees and State Machines / chapter 12 A Reusable, Light-Weight Finite-State Machine / chapter 13 Choosing Effective Utility-Based Considerations / chapter 14 Combining Scripted Behavior with Game Tree Search for Stronger, More Robust Game AI / section III Movement and Pathfinding -- chapter 15 Steering against Complex Vehicles in Assassins Creed Syndicate / chapter 16 Predictive Animation Control Using Simulations and Fitted Models / chapter 17 Fast Cars, Big City -- The AI of Driver San Francisco / chapter 18 A Unified Theory of Locomotion / chapter 19 RVO and ORCA -- How They Really Work / chapter 20 Optimization for Smooth Paths / chapter 21 3D Flight Navigation Using Sparse Voxel Octrees / chapter 22 Faster A* with Goal Bounding / chapter 23 Faster Dijkstra Search on Uniform Cost Grids / section IV Tactics and Strategy -- chapter 24 Being Where It Counts -- Telling Paragon Bots Where to Go / chapter 25 Combat Outcome Prediction for Real-Time Strategy Games / chapter 26 Guide to Effective Auto-Generated Spatial Queries / chapter 27 The Role of Time in Spatio-Temporal Reasoning -- Three Examples from Tower Defense / chapter 28 Pitfalls and Solutions When Using Monte Carlo Tree Search for Strategy and Tactical Games / chapter 29 Petri Nets and AI Arbitration / chapter 30 Hierarchical Portfolio Search in Prismata / section V Character Behavior -- chapter 31 Behavior Decision System -- Dragon Age Inquisitions Utility Scoring Architecture / chapter 32 Paragon Bots -- A Bag of Tricks / chapter 33 Using Your Combat AI Accuracy to Balance Difficulty / chapter 34 1000 NPCs at 60 FPS / chapter 35 Ambient Interactions -- Improving Believability by Leveraging Rule-Based AI / chapter 36 Stochastic Grammars -- Not Just for Words! …”