Tabla de Contenidos: “…4-3 Calculating Local Volatilities4-3.1 Dupire's Equation; 4-3.2 From Implied Volatility to Local Volatility; 4-3.3 Hedging with Local Volatility; 4-4 Stochastic Volatility; 4-4.1 Hedging Theory; 4-4.2 Connection with Local Volatility; 4-4.3 Monte Carlo Method; 4-4.4 Pricing and Hedging Forward Start Options; 4-4.5 A Word on Stochastic Volatility Models with Jumps; References; Problems; 4.1 From Implied to Local Volatility; 4.2 Market Price of Volatility Risk; 4.3 Local Volatility Pricing; Appendix 4.A: Derivation of Dupire's Equation; Chapter 5 Volatility Derivatives; 5-1 Volatility Trading…”

Tabla de Contenidos: “…Encoding using Huffman compression -- Remembering sequences with LZW -- Hiding Your Secrets with Cryptography -- Substituting characters -- Working with AES encryption -- Part 5 Challenging Difficult Problems -- Chapter 15 Working with Greedy Algorithms -- Deciding When It Is Better to Be Greedy -- Understanding why greedy is good -- Keeping greedy algorithms under control -- Considering NP complete problems -- Finding Out How Greedy Can Be Useful -- Arranging cached computer data -- Competing for resources -- Revisiting Huffman coding -- Chapter 16 Relying on Dynamic Programming -- Explaining Dynamic Programming -- Obtaining a historical basis -- Making problems dynamic -- Casting recursion dynamically -- Leveraging memoization -- Discovering the Best Dynamic Recipes -- Looking inside the knapsack -- Touring around cities -- Approximating string search -- Chapter 17 Using Randomized Algorithms -- Defining How Randomization Works -- Considering why randomization is needed -- Understanding how probability works -- Understanding distributions -- Simulating the use of the Monte Carlo method -- Putting Randomness into your Logic -- Calculating a median using quick select -- Doing simulations using Monte Carlo -- Ordering faster with quick sort -- Chapter 18 Performing Local Search -- Understanding Local Search -- Knowing the neighborhood -- Presenting local search tricks -- Explaining hill climbing with n-queens -- Discovering simulated annealing -- Avoiding repeats using Tabu Search -- Solving Satisfiability of Boolean Circuits -- Solving 2-SAT using randomization -- Implementing the Python code -- Realizing that the starting point is important -- Chapter 19 Employing Linear Programming -- Using Linear Functions as a Tool -- Grasping the basic math you need -- Learning to simplify when planning -- Working with geometry using simplex…”

Tabla de Contenidos: “…2.6.2 Hedge Ratio -- 2.6.3 Effectiveness Assessment -- 2.6.4 Effectiveness Assessment Methods -- 2.6.5 The Critical Terms Method -- 2.6.6 The Simple Scenario Analysis Method -- 2.6.7 The Regression Analysis Method -- 2.6.8 The Monte Carlo Simulation Method -- 2.6.9 Suggestions Regarding the Assessment Methods -- 2.7 The Hypothetical Derivative Simplification -- 2.8 Rebalancing -- 2.8.1 Accounting for Rebalancings -- 2.9 Discontinuation of Hedge Accounting -- 2.10 Options And Hedge Accounting -- 2.10.1 Intrinsic Value versus Time Value -- 2.10.2 In-, At- or Out-of-the-Money -- 2.10.3 Accounting Treatment for the Time Value of Options -- 2.10.4 Example of Option Hedging a Transaction Related Item - Actual Time Value Exceeding Aligned Time Value -- 2.10.5 Example of Option Hedging a Transaction Related Item - Actual Time Value Lower Than Aligned Time Value -- 2.10.6 Example of Option Hedging a Time-Period Related Item - Actual Time Value Exceeding Aligned Time Value -- 2.10.7 Example of Option Hedging a Time-Period Related Item - Actual Time Value Lower Than Aligned Time Value -- 2.10.8 Written Options -- 2.11 Forwards and Hedge Accounting -- Chapter 3 Fair Valuation - Credit and Debit Valuation Adjustments -- 3.1 Fair Valuation - Overview of IFRS 13 -- 3.1.1 Definition of Fair Value -- 3.1.2 Fair Value Hierarchy -- 3.1.3 Level 1 Financial Instruments -- 3.1.4 Level 2 Financial Instruments -- 3.1.5 Level 3 Financial Instruments -- 3.1.6 Mid-to-Bid and Mid-to-Offer Adjustments -- 3.1.7 Credit and Debit Valuation Adjustment -- 3.1.8 Funding Valuation Adjustment -- 3.1.9 Model Uncertainty Adjustment -- 3.1.10 Day 1 Profit (or Loss) -- 3.2 Case Study - Credit Valuation Adjustment of an Interest Rate Swap -- 3.2.1 Simple One-Period Model of Default -- 3.2.2 Working Example of CVA in a Swap -- 3.2.3 Debit Valuation Adjustments -- 3.2.4 Combining CVA and DVA…”

“…The first edition of Peter Lee's book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. …”

Tabla de Contenidos: “…La Radiación Cósmica; 2.6. Las Técnicas Monte-Carlo; Capítulo 3 FNAL E938: minería; 3.1. Objetivos; 3.2. …”

Tabla de Contenidos: “…ASIGNACIÓN DE ACTIVOS PARA UN PORTAFOLIO EFICIENTE EN PYTHON -- EVALUAR EL RENDIMIENTO DE UN 1 / N PORTAFOLIO BÁSICO -- ENCONTRAR LA FRONTERA EFICIENTE USANDO SIMULACIÓN MONTECARLO -- ENCONTRAR LA FRONTERA EFICIENTE USANDO OPTIMIZACIÓN CON SCIPY -- 12. SIMULACIÓN MONTE CARLO EN FINANZAS -- SIMULANDO LA DINÁMICA DEL PRECIO DE LAS ACCIONES UTILIZANDO MOVIMIENTO BROWNIANO GEOMÉTRICO -- PRECIOS DE OPCIONES EUROPEAS MEDIANTE SIMULACIONES…”

Tabla de Contenidos: “…Corrected Target Locations -- 5.2 Relationship Between Failures and TRE -- 5.3 Tighter Tolerances -- 6 Conclusions -- 7 Disclaimer -- References -- Assembly Cells and Systems -- Development of a Sensitive Winding Application Based on a Serial Robot and Integrated Torque Sensors -- 1 Introduction and Approach of the Problem -- 2 State of the Art of the Winding Application -- 3 Process Development -- 3.1 Winding Process -- 3.2 Feedback Control System -- 3.3 Measurement Concept -- 4 Implementation and Validation -- 5 Summary and Outlook -- References -- High-Load Titanium Drilling Using an Accurate Robotic Machining System -- 1 Introduction -- 2 Related Works -- 3 Accurate Robot Architecture -- 3.1 Kinematic Model -- 3.2 Spindle -- 3.3 Pressure Foot -- 3.4 Additional Sensors/Data Sources/Systems -- 3.5 Programmable Drilling Parameters -- 4 Industrial Applications -- 5 Experimental Methods -- 6 Results and Discussion -- 6.1 Dynamometer Results -- 6.2 Hole Quality -- 7 Conclusions -- References -- Application of Advanced Simulation Methods for the Tolerance Analysis of Mechanical Assemblies -- 1 Introduction -- 2 Tolerance Modelling -- 2.1 Case Study -- 2.2 Assembly Models -- 2.3 Probability of Defected Products and Limit State Function -- 3 Advanced Simulation Methods -- 3.1 Crude Monte Carlo -- 3.2 Latin Hypercube Simulation Method -- 3.3 Quasi Monte Carlo Simulation Based on Sobol' Sequence -- 3.4 Subset Simulation Method -- 4 Results and Discussion -- 5 Conclusions -- References -- Development of a Low-Cost, High Accuracy, Flexible Panel Indexing Cell with Modular, Elastic Architecture -- 1 Introduction -- 2 Objectives…”

Tabla de Contenidos: “…6.1.1 Wave-Energy Relationships -- 6.1.2 Diffuse Field Parameter of One-Dimensional Systems -- 6.1.3 Diffuse Field Parameter of Two-Dimensional Systems -- 6.1.4 Diffuse Field Parameter of Three-Dimensional Systems -- 6.1.5 Topology Conclusions -- 6.1.6 Auto Correlation and Boundary Effects -- 6.1.7 Sources in the Diffuse Acoustic Field - the Direct Field -- 6.1.8 Some Comments on the Diffuse Field Approach -- 6.2 Ensemble Averaging of Deterministic Systems -- 6.3 One-Dimensional Systems -- 6.3.1 Fluid Tubes -- 6.4 Two-Dimensional Systems -- 6.4.1 Plates -- 6.4.2 Monte Carlo Simulation -- 6.5 Three-Dimensional Systems - Cavities -- 6.5.1 Energy and Intensity -- 6.5.2 Power Input to the Reverberant Field -- 6.5.3 Dissipation -- 6.5.4 Power Balance -- 6.5.5 Monte Carlo Simulation -- 6.6 Surface Load of Diffuse Acoustic Fields -- 6.7 Mode Wave Duality -- 6.7.1 Diffuse Field Energy -- 6.7.2 Free Field Power Input -- 6.8 SEA System Description -- 6.8.1 Power Balance in Diffuse Fields -- 6.8.2 Reciprocity Relationships -- 6.8.3 Fluid Analogy -- 6.8.4 Power Input -- 6.8.5 Engineering Units -- 6.8.6 Multiple Wave Fields -- Bibliography -- 7 Coupled Systems -- 7.1 Deterministic Subsystems and their Degrees of Freedom -- 7.2 Coupling Deterministic Systems -- 7.2.1 Fluid Subsystems -- 7.2.2 Fluid Structure Coupling -- 7.2.3 Deterministic Systems Coupled to the Free Field -- 7.3 Coupling Random Systems -- 7.3.1 Power Input to System (m) from the nth Reverberant Field -- 7.3.2 Power Leaving the (m)th Subsystem -- 7.3.3 Some Remarks on SEA Modelling -- 7.4 Hybrid FEM/SEA Method -- 7.4.1 Combining SEA and FEM Subsystems -- 7.4.2 Work Flow of Hybrid Simulation -- 7.5 Hybrid Modelling in Modal Coordinates -- Bibliography -- 8 Coupling Loss Factors -- 8.1 Transmission Coefficients and Coupling Loss Factors -- 8.1.1 - Relationship from Diffuse Field Assumptions…”

Tabla de Contenidos: “…Decision Making Under Uncertainty 5.1 Types of Uncertainties 5.2 Assessing a Subjective Probability 5.3 Imprecise Probabilities 5.4 Cumulative Risk Profile and Dominance 5.5 Decision Trees: Modeling 5.6 Decision Trees: Determining Expected Values 5.7 Sequential Decision Making 5.8 Modeling Risk Aversion 5.9 Robustness 5.10 Uncertainty Propagation: Sensitivity Analysis 5.11 Uncertainty Propagation: Method of Moments 5.12 Uncertainty Propagation: Monte Carlo Simulation Exercises References 6. Game Theory 6.1 Game Theory Basics 6.2 Zero-sum Games 6.3 Optimal Mixed Strategies for Zero-sum Games 6.4 The Minimax Theorem 6.5 Resource Allocation Games 6.6 Mixed Motive Games 6.7 Bidding 6.8 Stackelberg Games Exercises References 7. …”

Tabla de Contenidos: “…Implementation in TensorFlow -- Deep belief networks -- Summary -- Chapter 5: Image Recognition -- Similarities between artificial and biological models -- Intuition and justification -- Convolutional layers -- Stride and padding in convolutional layers -- Pooling layers -- Dropout -- Convolutional layers in deep learning -- Convolutional layers in Theano -- A convolutional layer example with Keras to recognize digits -- A convolutional layer example with Keras for cifar10 -- Pre-training -- Summary -- Chapter 6: Recurrent Neural Networks and Language Models -- Recurrent neural networks -- RNN - how to implement and train -- Backpropagation through time -- Vanishing and exploding gradients -- Long short term memory -- Language modeling -- Word-based models -- N-grams -- Neural language models -- Character-based model -- Preprocessing and reading data -- LSTM network -- Training -- Sampling -- Example training -- Speech recognition -- Speech recognition pipeline -- Speech as input data -- Preprocessing -- Acoustic model -- Deep belief networks -- Recurrent neural networks -- CTC -- Attention-based models -- Decoding -- End-to-end models -- Summary -- Bibliography -- Chapter 7: Deep Learning for Board Games -- Early game playing AI -- Using the min-max algorithm to value game states -- Implementing a Python Tic-Tac-Toe game -- Learning a value function -- Training AI to master Go -- Upper confidence bounds applied to trees -- Deep learning in Monte Carlo Tree Search -- Quick recap on reinforcement learning -- Policy gradients for learning policy functions -- Policy gradients in AlphaGo -- Summary -- Chapter 8: Deep Learning for Computer Games -- A supervised learning approach to games -- Applying genetic algorithms to playing games -- Q-Learning -- Q-function -- Q-learning in action -- Dynamic games -- Experience replay -- Epsilon greedy…”

Tabla de Contenidos: “…7 Timeline: Events and Controller7.1 Events; 7.2 Timeline; 7.3 Controller; 8 The Hull-White Model; 8.1 A component-based design; 8.1.1 Requestor; 8.1.2 State; 8.1.3 Filler; 8.1.4 Rollback; 8.1.5 Evolve; 8.1.6 Exercise; 8.2 The model and model factories; 8.3 Concluding remarks; 9 Pricing using Numerical Methods; 9.1 A lattice pricing framework; 9.2 A Monte-Carlo pricing framework; 9.2.1 Pricing non-callable trades; 9.2.2 Pricing callable trades; 9.3 Concluding remarks; 10 Pricing Financial Structures in Hull-White; 10.1 Pricing a Bermudan; 10.2 Pricing a TARN; 10.3 Concluding remarks…”

Tabla de Contenidos: “…ANALYSIS OF SCENARIOS AND MODELING -- Scenarios: Connection to Policy -- Scenarios: Completeness and Coverage -- Scenarios: Physical Processes and Models -- Mathematical Models and Their Implementation -- Scenarios and Models: Parameter Values -- Models: Variability and Uncertainty -- Models: Sensitivity Analyses -- Models: Validation and Quality Control -- ECOLOGICAL SCENARIO -- 4 Issues of Model Application -- MODEL PARAMETERS -- Parameter Selection for Scenarios -- Food Intake -- Fruits and Vegetables -- Grains -- Meat, Dairy, and Eggs -- Fish -- PARAMETER SELECTION WITHIN SPECIFIC MODELS -- Soluble or Extractable Regulatory Thresholds -- Calculations for Lower SERTs -- Calculations for Upper SERTs -- Preliminary Endangerment Assessment -- Risk and Hazard Spreadsheets -- Exposure Pathway Factor for Inhalation -- Dust -- Quantitation of Intake for Workers -- Concentration limit in Waste -- Monte Carlo Analysis -- LeadSpread -- CalTOX -- Dust-Deposition Velocity -- Organic Carbon Content of Residential Soil -- Molecular Weight -- Chemical Properties -- ANALYTICAL METHODS -- TOXICITY TESTS -- Tests Related to Human Health -- Tests Related to Ecology -- 5 Meeting Program Goals -- RSU GUIDING PRINCIPLES -- Protect Public Health and Environment -- Regulatory Flexibility and Simplicity -- DTSC PROGRAM GOALS -- Considering Exposure in Classification of Waste -- Incorporating New Toxicological or Technical Data -- Developing a Mechanism for Regulating Chemicals Other Than the 36 SERT and 38 TTLC Chemicals -- OTHER CONSIDERATIONS FOR DTSC'S APPROACH -- PROGRAM EVALUATION -- References -- Appendix A Biographical Information on the Committee on Risk-Based Criteria for Non-RCRA Hazardous Waste -- Appendix B DTSC Issues -- Appendix C List of Public Access Materials Received by the NRC Committee on Risk-Based Criteria for Non-RCRA Hazardous Waste…”

Tabla de Contenidos: “…Emulating Natural Light Sources -- Sidebar: Source Models and Textures -- Mid-day Sun -- Sidebar: PBR Options -- Sunset -- Candle Flame -- Diffuse Window Light -- Emulating Artificial Light Sources -- Table Lamp -- Sidebar: Selective Shadow Casting -- Neon Sign -- Christmas Lights -- Lighting a Character with Different Light Sources -- Sidebar: Light and Shadow Linking -- Chapter 5: Working with PBR Systems -- Choosing PBR -- Review of Common Rendering Systems -- Scanline -- Ray Tracing -- Overview of PBR Systems -- Review of Common PBR Systems -- GI -- Photon Mapping -- Final Gather -- Radiosity -- Point Cloud -- Irradiance Cache -- Path Tracing / Monte Carlo Ray Tracing -- IBL -- Sky Systems -- Sidebar: Overview of Advanced 3D Renderers -- Sidebar: Unbiased vs. …”

Tabla de Contenidos: “…1 introduction 3 -- references 5 -- 2 intermediate filaments - from proteins to networks 9 -- 2.1 Structure and assembly of intermediate filament proteins 10 -- 2.2 Mechanical properties of single intermediate filaments 12 -- 2.2.1 Persistence length 12 -- 2.2.2 Stretching response . 13 -- 2.3 Networks of reconstituted intermediate filaments . 15 -- 2.4 Intermediate filament networks in cells . 16 -- 2.4.1 Structure and function of intermediate filament networks in cells 16 -- 2.4.2 Intermediate filament networks and cell mechanics 16 -- 2.4.3 Keratin networks in cells under load . 17 -- references 17 -- 3 biopolymer mechanics - theoretical and experimental principles 29 -- 3.1 Optical tweezers . 29 -- 3.1.1 Particle trapping 30 -- 3.1.2 Force detection . 31 -- 3.2 Microrheology 34 -- 3.2.1 Rheology of viscoelastic materials 34 -- 3.2.2 Passive microrheology: microparticle tracking . 34 -- 3.2.3 Active microrheology: optical trapping . 36 -- 3.3 Polymer mechanics . 38 -- 3.3.1 Entropic stretching of worm-like chains . 38 -- 3.3.2 Worm-like bundles . 39 -- 3.3.3 Networks of semiflexible polymers . 39 -- 3.4 Molecular reactions . 43 -- 3.4.1 Step-growth polymerization . 43 -- 3.4.2 Molecular reaction kinetics - two state models . 43 -- references 44 -- 4 materials and methods 51 -- 4.1 Vimentin preparation 51 -- 4.2 Maleimide functionalization of polystyrene beads . 52 -- 4.3 Stretching single filaments by optical trapping . 53 -- 4.4 Analysis of single filament mechanics 54 -- 4.5 Force-strain Monte-Carlo simulations 56 -- 4.6 Optical trap measurements of individual filament-filament interactions . 56 -- 4.7 Analysis of the interaction data 58 -- 4.8 Microrhelogy of reconstituted vimentin networks . 60 -- 4.9 Analysis of microrheology experiments . 61 -- 4.10 Imaging filament networks 63 -- 4.11 Imaging single filaments 63 -- 4.12 Analysis of filament lengths 64 -- 4.13 Finite element simulation of the microfluidic flowcell . 64 -- 4.14 Stretching MDCK II cells on elastic substrates . 65 -- 4.15 Analysis of images of stretched cells . 68 -- references 70 -- 5 tuning intermediate filament mechanics by variation of -- ph and ion charges 75 -- 5.1 Introduction . 76 -- 5.2 Results and discussion . 77 -- 5.2.1 Cations stiffen single vimentin IFs 77 -- 5.2.2 Stretching vimentin filament bundles 81 -- 5.2.3 IF mechanics adapt to pH changes 82 -- 5.2.4 IF stiffening saturates at low pH . 83 -- 5.2.5 Variations in the free energy landscapes influence filament mechanics 85 -- 5.3 Conclusions . 90 -- references 91 -- 6 multiscale mechanics and temporal evolution of vimentin -- intermediate filament networks 97 -- 6.1 Introduction . 98 -- 6.2 Results and discussion . 98 -- 6.2.1 Vimentin filament networks mature and stiffen on time scales of days 98 -- 6.2.2 The filament length depends on elongation and lateral association 100 -- 6.2.3 Electrostatic and hydrophobic interactions lead to mechanically distinct networks 101 -- 6.2.4 Maturation of networks is concentration dependent 104 -- 6.2.5 Surface effects modify network structures . 105 -- 6.2.6 Single filament mechanics are unaffected by detergents or divalent ions . 107 -- 6.2.7 Electrostatics increase single filament-filament interactions108 -- 6.2.8 Interactions are independent of binding-site encounter rate112 -- 6.2.9 A two-state model accurately describes network mechanics112 -- 6.3 Conclusions . 113 -- 6.4 Outlook 115 -- 6.4.1 Entropic and elastic stretching of single vimentin filaments115 -- 6.4.2 Single interactions of pre-strained filaments 117 -- references 119 -- 7 response of actin and keratin structures to isotropic cell stretching 125 -- 7.1 Introduction . 125 -- 7.2 Results and Discussion . 126 -- 7.2.1 Equibiaxial stretching of PDMS devices . 126 -- 7.2.2 The cell area increases during isotropic stretching . 128 -- 7.2.3 Actin stress fibers disassemble at increasing cell extension 129 -- 7.2.4 The keratin structure adapts to increasing strains . 131 -- 7.3 Conclusion 133 -- references 133 -- 8 discussion and conclusion 137 -- references 140 -- appendix 145 -- a supporting information: tuning intermediate filament mechanics by variation of ph and ion charges 145 -- a.1 Flow simulations 145 -- a.2 Single force-strain curves . 147 -- b supporting information: multiscale mechanics and temporal evolution of vimentin intermediate filament networks 149 -- b.1 Additional information for elongation measurements . 149 -- b.2 Data analysis of microrheology measurements . 152 -- b.3 Modeling single interactions 161 -- c supporting information: response of actin and keratin structures to isotropic cell stretching 167 -- references 169 -- Acknowledgments 172 -- List of acronyms 174 -- Publications 176.…”

Tabla de Contenidos: “…7.5 Common terminology -- 7.6 Drawbacks of differential signaling -- 7.7 References -- 7.8 Problems -- Chapter 8: Mathematical Requirements of Physical Channels -- 8.1 Frequency domain effects in time domain simulations -- 8.2 Requirements for a physical Channel -- 8.3 References -- 8.4 Problems -- Chapter 9: Network Analysis for Digital Engineers -- 9.1 High frequency voltage and current waves -- 9.2 Network Theory -- 9.3 Properties of Physical S-parameters -- 9.4 References -- 9.5 Problems -- Chapter 10: Topics in High-Speed Channel Modeling -- 10.1 Creating a physical transmission line mode -- 10.2 Non-Ideal Return Paths -- 10.3 Vias -- 10.4 References -- 10.5 Problems -- Chapter 11: I/O Circuits and Models -- 11.1 Introduction -- 11.2 Push-Pull Transmitters -- 11.3 CMOS Receivers -- 11.4 ESD Protection Circuits -- 11.5 On-Chip Termination -- 11.6 Bergeron Diagrams -- 11.7 Open Drain Transmitters -- 11.8 Differential Current Mode Transmitters -- 11.9 Low Swing/Differential Receivers -- 11.10 IBIS Models -- 11.11 Summary -- 11.12 References -- 11.13 Problems -- Chapter 12: Equalization -- 12.1 Introduction -- 12.2 Continuous Time Linear Equalizers -- 12.3 Discrete Linear Equalizers -- 12.4 Decision Feedback Equalization -- 12.5 Summary -- 12.6 References -- 12.7 Problems -- Chapter 13: Modeling and Budgeting of Timing Jitter and Noise -- 13.1 The Eye Diagram -- 13.2 Bit Error Rate -- 13.3 Jitter Sources and Budgets -- 13.4 Noise Sources and Budgets -- 13.5 Peak Distortion Analysis Methods -- 13.6 Summary -- 13.7 References -- 13.8 Problems -- Chapter 14: System Analysis Using Response Surface Modeling -- 14.1 Introduction -- 14.2 Case Study: 10 Gb/s differential PCB interface -- 14.3 RSM Construction by Least Squares Fitting -- 14.4 Measures of Fit -- 14.5 Significance Testing -- 14.6 Confidence Intervals -- 14.7 Sensitivity Analysis and Design Optimization -- 14.8 Defect Rate Prediction Using Monte Carlo Simulation -- 14.9 Additional RSM Considerations -- 14.10 Summary…”

Tabla de Contenidos: “…-- 24.5 Example: The MNIST Handwriting Dataset -- 24.6 Recurrent Neural Networks -- 24.7 Bayesian Networks -- 24.8 Training and Prediction -- 24.9 Markov Chain Monte Carlo -- 24.10 PyMC Example -- 24.11 Further Reading -- 24.12 Glossary -- Chapter 25 Stochastic Modeling -- 25.1 Markov Chains -- 25.2 Two Kinds of Markov Chain, Two Kinds of Questions -- 25.3 Markov Chain Monte Carlo -- 25.4 Hidden Markov Models and the Viterbi Algorithm -- 25.5 The Viterbi Algorithm -- 25.6 Random Walks -- 25.7 Brownian Motion…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Plenary lectures -- Molecular dynamics-based structural mechanics of buildings' resilience -- Century-long durability of concrete structures: Expansiveness of hydration and chemo-mechanics of autogenous shrinkage and swelling -- Regularized continuum damage formulations acting as localization limiters -- Erection of bridges and shells without formwork-challenges for the computational modelling -- Network modelling of fracture processes in fibre-reinforced quasi-brittle materials -- New damage model to simulate ballistic impact on concrete targets -- Multiscale cement and concrete research: Experiments and modeling -- Fishnet model for failure probability of nacre-like imbricated lamellar materials and Monte Carlo verification -- Phase-field modeling of cement paste: Where particle physics meets continuum mechanics -- Towards a mesoscale model of geopolymers: Interaction potential from the molecular scale -- Nanoscale simulations of cement hydrates precipitation mechanisms: Impact on macroscopic self-desiccation and water sorption isotherms -- Atomistic modeling of early hydration of C3S -- Modeling the evolution of C3S-C3S grain interface over hydration time -- Fracture properties of cement hydrates determined from microbending tests and multiscale modeling -- Testing and modelling of micro cement paste cube under indentation splitting -- An adaptive concurrent two-scale FE model to predicting crack propagation in concrete -- Multi-scale modelling of the mechanics of concrete based on the cement paste properties -- Sensitivity estimation of cement paste properties in the microstructural characteristics -- Elastic-plastic multi scale approach for localization problems-the embedded unit cell…”

Tabla de Contenidos: “…Carrying Out the Gaussian Hidden Markov Model -- Considering the Hidden States in US Confirmed COVID-19 Cases with the Gaussian Hidden Markov Model -- Simulating US Confirmed COVID-19 Cases with the Monte Carlo Simulation Method -- US Confirmed COVID-19 Cases Simulation Results -- Conclusion -- Chapter 4: Cancer Segmentation with Neural Networks -- Exploring Cancer -- Exploring Skin Cancer -- Classifying Patient Skin Cancer Outcomes by Executing a CNN -- A CNN Pipeline -- A CNN's Architectural Structure -- Classifying Skin Cancer Diagnosis Image Data by Executing a CNN -- Preprocessing the Training Skin Cancer Image Data -- Preprocessing the Validation Skin Cancer Image Data -- Generating the Training Skin Cancer Diagnosis Image Data -- Tuning the Training Skin Cancer Image Data -- Executing the CNN to Classify Skin Cancer Diagnosis Image Data -- Considering the CNN's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation -- Sparse Categorical Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Visible Presence of Breast Cancer -- Classifying Ultrasound Scans of Breast Cancer Patients by Executing a CNN -- Preprocessing the Validation Breast Cancer Image Data -- Generating the Training Breast Cancer Diagnosis Image Data -- Tuning the Training Breast Cancer Image Data -- Executing the CNN to Classify Breast Cancer Diagnosis Image Data -- Considering the CNN's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation -- Sparse Categorical Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Conclusion -- Chapter 5: Modeling Magnetic Resonance Imaging and X-Rays by Executing Artificial Neural Networks -- Brain Tumors -- MRI Procedure -- Preprocessing the Training MRI Image Data -- Preprocessing the Validation MRI Image Data…”

Tabla de Contenidos: “…Autoregressive models -- Moving average models -- Autoregressive moving average model -- Autoregressive integrated moving average models -- Removing seasonality from a time series -- Analyzing a time series dataset -- Identifying a trend in a time series -- Time series decomposition -- Additive method -- Multiplicative method -- LSTM for time series analysis -- Overview of the time series dataset -- Data scaling -- Data splitting -- Building the model -- Making predictions -- Summary -- Chapter 15: Reinforcement Learning -- Reinforcement learning introduction -- Agent-Environment interface -- Markov Decision Process -- Discounted cumulative reward -- Exploration versus exploitation -- Reinforcement learning techniques -- Q-learning -- Temporal difference learning -- Dynamic Programming -- Monte Carlo methods -- Deep Q-Network -- OpenAI Gym -- Cart-Pole system -- Learning phase -- Testing phase -- Summary -- Chapter 16: Generative Neural Networks -- Unsupervised learning -- Generative models -- Restricted Boltzmann machine -- Boltzmann machine architecture -- Boltzmann machine disadvantages -- Deep Boltzmann machines -- Autoencoder -- Variational autoencoder -- Generative adversarial network -- Adversarial autoencoder -- Feature extraction using RBM -- Breast cancer dataset -- Data preparation -- Model fitting -- Autoencoder with Keras -- Load data -- Keras model overview -- Sequential model -- Keras functional API -- Define model architecture -- Magenta -- The NSynth dataset -- Summary -- Chapter 17: Chatbots -- Chatbots fundamentals -- Chatbot history -- The imitation game -- Eliza -- Parry -- Jabberwacky -- Dr. …”

Tabla de Contenidos: “…Annexure 9.2-Illustration of Heuristics -- Annexure 9.3-Illustration for Empirical Evaluation of Greedy Heuristics -- Annexure 9.4-Illustration for Monte Carlo Simulation -- Annexure 9.5-Illustration for Simulation from Actual Research -- Suggested Readings -- Questions and Exercises -- Part D: Research Design for Data Acquisition -- Chapter 10: Measurement Design -- Introduction -- Primary Types of Measurement Scales -- Nominal Scales -- Ordinal Scales -- Interval Scales -- Ratio Scales -- Errors in Measurement -- Validity and Reliability in Measurement -- Validity of Measurement -- Reliability in Measurement -- Types of Scaling (Scale Classification) -- Response Methods -- Quantitative Judgment Methods -- Scale Construction Techniques -- Judgment Methods -- Factor Scales -- Summary -- Annexure 10.1-Illustrative Example: Content Validity -- Annexure 10.2-Illustrative Example: Concurrent and External Validity -- Annexure 10.3-Illustrative Example: Construct Validity -- Annexure 10.4-Illustrative Example: Reliability in Measurement -- Suggested Readings -- Questions and Exercises -- Chapter 11: Sample Design -- Introduction -- Sampling Process -- Non-Probability Sampling -- Probability Sampling -- Simple Random Sampling -- Stratified Random Sampling -- Cluster Sampling -- Systematic Random Sampling -- Area Sampling -- Determination of Sample Size -- Required Size/Cell -- Use of Statistical Models -- Bayesian Method for Determination of Sample Size -- Illustrative Examples of Sample Size Determination -- Summary -- Suggested Readings -- Questions and Exercises -- Part E: Acquisition and Preparation of Research Data -- Chapter 12: Data Collection Procedures -- Introduction -- Sources of Secondary Data -- Internal Sources -- External Sources -- Computer Search for Secondary Data -- Primary Data Collection Methods -- Observation…”