Acceso restringido usuarios UPSA

“…Three appendices provide a review of basic probability concepts, probability distributions, and Markov models; an introduction to optimization models; and a discussion of Monte-Carlo simulations. This is a book that can be used as a military manual, reference book, and textbook for military courses on this vital subject. …”

“…In addition to many papers on Bayesian statistics, simulation methods, and decision theory, he has written four other books, including Monte Carlo Statistical Method (Springer 2004) with George Casella and Bayesian Core (Springer 2007) with Jean-Michel Marin. …”

“…Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. …”

“…You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. …”

Tabla de Contenidos: “…Higurashi-Hirunuma 179 -Monte Carlo simulation of beam mis-steering at electron accelerators by M.S. …”

Tabla de Contenidos: “…Machine generated contents note: Preface Acknowledgments Chapter 1: Discount Rates and Returns Estimating Returns Geometric and Arithmetic Averages Caveats to Return Extrapolation Discounting Present Values of Cash Flow Streams Internal Rate of Return and Yield to Maturity Real and Nominal Returns Summary Chapter 2: Fixed Income Securities Coupon Bearing Bonds Infinite Cash Flow Streams (Perpetuities) General Pricing Formulas for Finite Cash Flow Streams Interest Rate Risk Analysis of Duration Interest Rate Risk Dynamics Immunization and Duration Applications - Liability Discounting and Cash Matching Pension Logic Risky Coupons Inflation Risk and TIPS A Bond Portfolio Strategy (Optional) Summary Appendix 2.1: Solving Infinite and Finite Power Series References Chapter 3: Term Structure Discounting Using Spot Rates Forward Rates NPV revisited Short Rates The Bootstrap Method Duration Redux Summary Chapter 4: Equity The Determination of Stock Prices Discount Rates Redux Price and Dividend Multiples Extrapolating Multiples to Forecast Returns Pitfalls of Trend Analysis The Gordon Growth Model Sources of Return Summary References Chapter 5: Portfolio Construction Stochastic Returns and Risk Diversification The Efficient Frontier Markowitz Portfolio Selection Criteria Capital Market Line and the CAPM Performance Evaluation Summary Appendix 5.1: Statistical Review Appendix 5.2: Risk Adjusted Performance References Chapter 6: Optimal Portfolios Portfolio 1: Minimum Variance Portfolio (Fully Invested) Portfolio 2: Minimum Variance Portfolios with Targeted Return Portfolio 3: Minimum Variance Portfolios with No Short Sales Portfolio 4: Minimum Variance Portfolios with Capped Allocations Portfolio 5: Maximum Risk-Adjusted Return Performance Attribution The Efficient Frontier (Again) Summary Appendix 6.1: Matrix Operations Chapter 7: Data and Applications Analyzing Returns on a Ten Asset Portfolio Performance Attribution Changing the Investment Horizon Benchmarking to the Market Portfolio The Cost of Constraints A Bond Strategy Summary Chapter 8: Anomalies Deviations from the CAPM Behavioral Finance Summary References Chapter 9: Factor Models Arbitrage Pricing Theory (APT) Factor Selection Model Estimation Principal Components Applications and Examples Summary References Chapter 10: Active Portfolio Management Active Portfolio Construction and Attribution Analysis Performance Attribution Summary Appendix 10.1: Active Space Chapter 11: Risk The Failure of VaR Taxonomy of Risk Visualizing Risk Estimating Volatilities Maximum Likelihood Estimation (Optional) Credit Risk Adjusting for Leverage Adjusting for Illiquidity Other Risks Summary References Chapter 12: Monte Carlo Methods Example 1: Generating Random Numbers - Estimating pi Example 2: Confirming the Central Limit Theorem Example 3: Credit Default Risk Non-Normal Distributions The Gaussian Copula Summary References Chapter 13: Systemic Risk Extreme Value Theory Estimating the Hazards of Downside Risks A Systemic Risk Indicator Summary References Chapter 14: Incorporating Subjective Views Methodological Concepts An Example using Black-Litterman Active Space Risk Attribution Summary References Chapter 15: Futures, Forwards, and Swaps Institutional Detail and Futures Mechanics The Relationship between Spot Prices and Forward (Futures) Prices Hedging Basis Risk Hedging Portfolio Risk Futures Pricing Swaps Summary References Chapter 16: Introduction to Options Option Payoffs and Put-Call Parity Pricing European Call Options Pricing European Put Options Option Strategies Real Options Summary References Chapter 17: Models of Stock Price Dynamics Stock Price Dynamics Ito Processes Lognormal Stock Prices Deriving the Parameters of the Binomial Lattice Black-Scholes-Merton Model The Greek Letters Monte Carlo Methods Summary Appendix 17.1: Derivation of Ito's Lemma Chapter 18: Hedging Portfolio Risk Simple Hedging Strategies S&P 500 Index Puts Selling Volatility VIX Calls Liability Driven Investment Summary References Chapter 19: Private Equity The Private Equity Model Return and Risk Methodology Summary Appendix 19.1: CAPM References Chapter 20: Structured Credit Securitization Credit Enhancement Basics of Pricing Interest Rate Derivatives Interest Rate Dynamics CDO Valuation The Crash of the Housing Bubble Summary References Chapter 21: Optimal Rebalancing Trigger Strategies and No-trade Regions An Optimal Control Problem Implications Optimal Rebalancing in a Static Optimization Model The Comparative Statics of Transactions Costs References Chapter 22: Data Problems* Covariance Estimation An Example Empirical Results Overlapping Observations Conclusions Appendix 22.1: Covariance Matrix Estimation Removing the effects of smoothing References About the Author Index…”

“…Papers include original results of symmetric random walks and their characterization, stochastic processes, stochastic integrals, martingales, probability inequalities, statistics parameter estimation, stochastic differential equations, fractional Brownian motions, continuous time random walk models, anomalous diffusion models, Black-Scholes models, Monte Carlo methods, etc. This Special Issue is focused on concepts and techniques and is oriented toward a broad spectrum of applied mathematics and sciences. …”

Tabla de Contenidos: “…EXPLORING THE FERMI PARADOX -- Project #17: Modeling the Milky Way -- The Strategy -- Estimating the Number of Civilizations -- Selecting Radio Bubble Dimensions -- Generating a Formula for the Probability of Detection -- The Probability-of-Detection Code -- Building the Graphical Model -- Results -- Summary -- Further Reading -- Practice Projects -- Challenge Projects -- 11 THE MONTY HALL PROBLEM -- Monte Carlo Simulation -- Project #18: Verify vos Savant -- Project #19: The Monty Hall Game -- Summary -- Further Reading -- Practice Project: The Birthday Paradox -- 12 SECURING YOUR NEST EGG -- Project #20: Simulating Retirement Lifetimes -- The Strategy -- The Pseudocode -- Finding Historical Data -- The Code -- Using the Simulator -- Summary -- Further Reading -- Challenge Projects -- 13 SIMULATING AN ALIEN VOLCANO -- Project #21: The Plumes of Io -- A Slice of pygame -- The Strategy -- The Code -- Running the Simulation -- Summary -- Further Reading -- Practice Project: Going the Distance -- Challenge Projects -- 14 MAPPING MARS WITH THE MARS ORBITER -- Astrodynamics for Gamers -- Project #22: The Mars Orbiter Game -- The Strategy -- Game Assets -- The Code -- Summary -- Challenge Projects -- 15 IMPROVING YOUR ASTROPHOTOGRAPHY WITH PLANET STACKING -- Project #23: Stacking Jupiter -- The pillow Module…”

Tabla de Contenidos: “…Regression ComponentsBuilding DLMs with Components; Recursive Relationships in the DLM; Filtering Recursion; Smoothing Recursion; Predictive Distributions and Forecasting; Variance Estimation; Univariate Case; Multivariate Case; Sequential Model Comparison; Chapter 6 Sequential Monte Carlo Inference; Nonlinear and Non-Normal Models; Gibbs Sampling; Forward-Filtering Backward-Sampling; State Learning with Particle Filters; The Particle Set; A First Particle Filter: The Bootstrap Filter; The Auxiliary Particle Filter; Joint Learning of Parameters and States; The Liu-West Filter…”

Tabla de Contenidos: “…-- Checking Data-Race Freedom of GPU Kernels, Compositionally -- GenMC: A Model Checker for Weak Memory Models -- Hybrid and Cyber-Physical Systems -- Synthesizing Invariant Barrier Certificates via Difference-of-Convex Programming -- An Iterative Scheme of Safe Reinforcement Learning for Nonlinear Systems via Barrier Certificate Generation -- HybridSynchAADL: Modeling and Formal Analysis of Virtually Synchronous CPSs in AADL -- Computing Bottom SCCs Symbolically Using Transition Guided Reduction -- Implicit Semi-Algebraic Abstraction for Polynomial Dynamical Systems -- IMITATOR 3: Synthesis of timing parameters beyond decidability -- Formally Verified Switching Logic for Recoverability of Aircraft Controller -- SceneChecker: Boosting Scenario Verification using Symmetry Abstractions -- Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness -- Fast zone-based algorithms for reachability in pushdown timed automata -- Security -- Verified Cryptographic Code for Everybody -- Not All Bugs Are Created Equal, But Robust Reachability Can Tell The Difference -- A Temporal Logic for Asynchronous Hyperproperties -- Product Programs in the Wild: Retrofitting Program Verifiers to Check Information Flow Security -- Constraint-based Relational Verification -- Pre-Deployment Security Assessment for Cloud Services through Semantic Reasoning -- Synthesis -- Synthesis with Asymptotic Resource Bounds -- Program Sketching by Automatically Generating Mocks from Tests -- Counterexample-Guided Partial Bounding for Recursive Function Synthesis -- PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs -- Adapting Behaviors via Reactive Synthesis -- Causality-based Game Solving…”

Tabla de Contenidos: “…-- The Gaussian mixture model -- Definition -- Summary -- Chapter 5: Approximate Inference -- Sampling from a distribution -- Basic sampling algorithms -- Standard distributions -- Rejection sampling -- An implementation in R -- Importance sampling -- An implementation in R -- Markov Chain Monte-Carlo -- General idea of the method -- The Metropolis-Hastings algorithm -- MCMC for probabilistic graphical models in R -- Installing Stan and RStan -- A simple example in RStan -- Summary -- Chapter 6: Bayesian Modeling - Linear Models -- Linear regression -- Estimating the parameters -- Bayesian linear models -- Over-fitting a model -- Graphical model of a linear model -- Posterior distribution -- Implementation in R -- A stable implementation -- More packages in R -- Summary -- Chapter 7: Probabilistic Mixture Models -- Mixture models -- EM for mixture models -- Mixture of Bernoulli -- Mixture of experts -- Latent Dirichlet Allocation -- The LDA model -- Variational inference -- Examples -- Summary -- Appendix -- References -- Books on the Bayesian theory -- Books on machine learning -- Papers -- Index…”

Tabla de Contenidos: “…1 Basic data analysis -- R programming conventions -- Generation of random numbers and patterns -- Random numbers -- Patterns -- Case study: distribution diagnostics -- Distribution functions -- Histograms -- Barcharts -- Statistics of distribution functions; Kolmogorov-Smirnov tests -- Monte Carlo confidence bands -- Statistics of histograms and related plots; X2-tests -- Moments and quantiles -- R complements -- Random numbers -- Graphical comparisons -- Functions -- Enhancing graphical displays -- R internals -- parse -- eval -- print -- Executing files -- Packages -- Statistical summary -- Literature and additional references -- 2 Regression -- General regression model -- Linear model -- Factors -- Least squares estimation -- Regression diagnostics -- More examples for linear models -- Model formulae -- Gauss-Markov estimator and residuals -- Variance decomposition and analysis of variance -- Simultaneous inference -- Scheff́e's confidence bands -- Tukey's confidence intervals -- Case study: titre plates -- Beyond linear regression -- Transformations -- Generalised linear models -- Local regression -- R complements -- Discretisation -- External data -- Testing software -- R data types -- Classes and polymorphic functions -- Extractor functions -- Statistical summary -- Literature and additional references --…”

Tabla de Contenidos: “…Stability IssuesCalibration Frequency; Stochastic Volatility; Stochastic Volatility Processes; GARCH and Diffusion Limits; The Pricing PDE under Stochastic Volatility; The Market Price of Volatility Risk; The Two-Factor PDE; The Generalized Fourier Transform; The Transform Technique; Special Cases; The Mixing Solution; The Romano Touzi Approach; A One-Factor Monte-Carlo Technique; The Long-Term Asymptotic Case; The Deterministic Case; The Stochastic Case; A Series Expansion on Volatility-of-Volatility; Local Volatility Stochastic Volatility Models; Stochastic Implied Volatility…”

Tabla de Contenidos: “…Nonlinear Formulation -- Solving Nonlinear Regression Model -- Estimating Parameters -- Nonlinear least squares (Non-OLS) -- Hypothesis Testing -- Evaluate Model Performance -- Sampling Techniques -- Random Sampling -- Sampling With Replacement -- Sampling Without Replacement -- Monte Carlo Simulation -- Bootstrapping Techniques -- Jackknife Sampling Techniques -- Chapter 9. …”

Tabla de Contenidos: “…7.8 Minimal Sufficiency and Ancillary Statistics -- 7.9 Sufficiency, Completeness, and Independence -- 8 Optimal Tests of Hypotheses -- 8.1 Most Powerful Tests -- 8.2 Uniformly Most Powerful Tests -- 8.3 Likelihood Ratio Tests -- 8.3.1 Likelihood Ratio Tests for Testing Means of Normal Distributions -- 8.3.2 Likelihood Ratio Tests for Testing Variances of Normal Distributions -- 8.4 *The Sequential Probability Ratio Test -- 8.5 *Minimax and Classification Procedures -- 8.5.1 Minimax Procedures -- 8.5.2 Classification -- 9 Inferences About Normal Linear Models -- 9.1 Introduction -- 9.2 One-Way ANOVA -- 9.3 Noncentral χ2 and F-Distributions -- 9.4 Multiple Comparisons -- 9.5 Two-Way ANOVA -- 9.5.1 Interaction Between Factors -- 9.6 A Regression Problem -- 9.6.1 Maximum Likelihood Estimates -- 9.6.2 *Geometry of the Least Squares Fit -- 9.7 A Test of Independence -- 9.8 The Distributions of Certain Quadratic Forms -- 9.9 The Independence of Certain Quadratic Forms -- 10 Nonparametric and Robust Statistics -- 10.1 Location Models -- 10.2 Sample Median and the Sign Test -- 10.2.1 Asymptotic Relative Efficiency -- 10.2.2 Estimating Equations Based on the Sign Test -- 10.2.3 Confidence Interval for the Median -- 10.3 Signed-Rank Wilcoxon -- 10.3.1 Asymptotic Relative Efficiency -- 10.3.2 Estimating Equations Based on Signed-Rank Wilcoxon -- 10.3.3 Confidence Interval for the Median -- 10.3.4 Monte Carlo Investigation -- 10.4 Mann-Whitney-Wilcoxon Procedure -- 10.4.1 Asymptotic Relative Efficiency -- 10.4.2 Estimating Equations Based on the Mann-Whitney- Wilcoxon -- 10.4.3 Confidence Interval for the Shift Parameter Δ -- 10.4.4 Monte Carlo Investigation of Power -- 10.5 *General Rank Scores -- 10.5.1 Efficacy -- 10.5.2 Estimating Equations Based on General Scores -- 10.5.3 Optimization: Best Estimates -- 10.6 *Adaptive Procedures -- 10.7 Simple Linear Model…”

Tabla de Contenidos: “…Exercises -- Exercise 1 -- Exercise 2 -- Chapter 10: Monte Carlo Analysis -- 10.1. Simulation Settings -- 10.1.1. …”

“…This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. …”

“…Mathematical Modeling of stochastic processes is based on the probability theory, in particular, that leads to using of random walks, Monte Carlo methods and the standard statistics tools. …”

Tabla de Contenidos: “…Setting up the software and connecting -- Troubleshooting -- Calibrating and getting readings -- Calibration code -- The calibration process -- Always face North behavior -- CircuitPython code for the face North behavior -- Troubleshooting -- Making a known turn behavior -- Summary -- Exercises -- Further reading -- Chapter 13: Determining Position Using Monte Carlo Localization -- Technical requirements -- Creating a training area for our robot -- What we will make -- How we will make the arena -- Tips for cutting -- Modeling the space -- Representing the arena and robot position as numbers -- Serving the arena from the robot -- The Bleak library -- Creating a Bluetooth LE wrapper library -- Showing the robot's data on the computer screen -- Using sensors to track relative pose -- Setting up poses -- Displaying poses -- Moving with collision avoidance -- Moving poses with the encoders -- Pose movement probabilities -- Monte Carlo localization -- Generating pose weights from a position -- Resampling the poses -- Incorporating distance sensors -- Tuning and improving the Monte Carlo model -- Summary -- Exercises -- Further reading -- Chapter 14: Continuing Your Journey - Your Next Robot -- Technical requirements -- A summary of what you have learned in this book -- Basic robotics with Raspberry Pi Pico -- Extending a Raspberry Pi Pico robot with sensors -- Writing CircuitPython behavior code for Raspberry Pi Pico -- Planning to extend this robot -- Sensors you could add -- Interacting with the robot -- Chassis and form enhancements -- Electronics enhancements -- Outputs you could add -- Extending the code and behaviors -- Planning your next robot -- Form, shape, and chassis -- Electronics and sensors -- Code and behavior -- Further suggested areas to learn about -- Electronics -- Design and manufacturing -- Robotic competitions and communities…”