“…Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. …”

“…The main prerequisites are knowledge of Bayesian statistics and basic Markov chain Monte Carlo methods. A number of real-world examples are thoroughly analyzed in order to demonstrate the methods and to assist the readers in applying these methods to their own work. …”

“…Solve real business problems with Excel—and build your competitive advantage Quickly transition from Excel basics to sophisticated analytics Summarize data by using PivotTables and Descriptive Statistics Use Excel trend curves, multiple regression, and exponential smoothing Master advanced functions such as OFFSET and INDIRECT Delve into key financial, statistical, and time functions Leverage the new charts in Excel 2016 (including box and whisker and waterfall charts) Make charts more effective by using Power View Tame complex optimizations by using Excel Solver Run Monte Carlo simulations on stock prices and bidding models Work with the AGGREGATE function and table slicers Create PivotTables from data in different worksheets or workbooks Learn about basic probability and Bayes’ Theorem Automate repetitive tasks by using macros…”

“…The rst one emphasized the importance of linear computations, and the second one the importance of massive computations, using random number generations (and Monte Carlo methods), while (at that time) access to digital computers was not widespread--…”

“…You will: Apply regression techniques to find the pattern for depreciation of the value of cars over a period of years Work with the product moment correlation coefficient technique to illustrate the accuracy (or otherwise) of regression techniques Use the past stock values of an asset to predict what its future values may be using Monte Carlo methods Simulate the buying of supermarket stock by shoppers and check the remaining stock: if it is too low print a message to reorder the stock Create a file of arrivals for an airport and send data to the airport’s display boards to show the current situation for the incoming flights Simulate the patterns of particles moving in gases or solids…”

Tabla de Contenidos: “…B.3.7 Histogram 478 -- B.3.8 Parametric Analysis 478 -- B.3.9 Monte Carlo Analysis 479 -- B.4 Optimization Interface 480 -- B.5 Tutorial Example: Using SIMSIDES to Model and Analyze ΣΔMs 482 -- B.5.1 Creating the Cascade 2-1 ΣΔM Block Diagram in SIMSIDES 482 -- B.5.2 Setting Model Parameters 482 -- B.5.3 Computing the Output Spectrum 484 -- B.5.4 SNR versus Input Amplitude Level 486 -- B.5.5 Parametric Analysis Considering Only One Parameter 487 -- B.5.6 Parametric Analysis Considering Two Parameters 488 -- B.5.7 Computing Histograms 489 -- B.6 Getting Help 489 -- C SIMSIDES Block Libraries and Models 491 -- C.1 Overview of SIMSIDES Libraries 491 -- C.2 Ideal Libraries 492 -- C.2.1 Ideal Integrators 492 -- C.2.1.1 Building-block Model Purpose and Description 492 -- C.2.1.2 Model Parameters 493 -- C.2.2 Ideal Resonators 493 -- C.2.2.1 Ideal_LD_Resonator 493 -- C.2.2.2 Ideal_FE_Resonator 493 -- C.2.2.3 Ideal_CT_Resonator 493 -- C.2.3 Ideal Quantizers 494 -- C.2.3.1 Ideal_Comparator 494 -- C.2.3.2 Ideal_Comparator_for_SI 495 -- C.2.3.3 Ideal_Multibit_Quantizer 495 -- C.2.3.4 Ideal_Multibit_Quantizer_for_SI 496 -- C.2.3.5 Ideal_Multibit_Quantizer_levels 496 -- C.2.3.6 Ideal_Multibit_Quantizer_levels_SD2 496 -- C.2.3.7 Ideal_Sampler 496 -- C.2.4 Ideal D/A Converters 496 -- C.2.4.1 Ideal_DAC_for_SI 496 -- C.2.4.2 Ideal_DAC_dig_level_SD2 497 -- C.3 Real SC Building-Block Libraries 497 -- C.3.1 Real SC Integrators 497 -- C.3.2 Real SC Resonators 501 -- C.4 Real SI Building-Block Libraries 503 -- C.4.1 Real SI Integrators 503 -- C.4.2 Real SI Resonators 505 -- C.4.3 SI Errors and Model Parameters 506 -- C.4.3.1 Basic_SI_FE(LD)_Integrator and Basic_SI_FE(LD)_Resonator 506 -- C.4.3.2 SI_FE(LD)_Int_Finite_Conductance 507 -- C.4.3.3 SI_FE(LD)_Int_Finite_Conductance & Settling & ChargeInjection 508 -- C.5 Real CT Building-Block Libraries 508 -- C.5.1 Real CT Integrators 508 -- C.5.1.1 Model Parameters used in Transconductors and Gm-C Integrator Building Blocks 511.…”

“…Students will learn how to: • Set measurable project objectives and create a practical plan to achieve them • Control time, cost, and scope • Obtain approval and buy-in from senior management and key stakeholders • Create a work breakdown structure to ensure a solid foundation for project activities • Use network diagramming to control the project schedule • Employ standard estimating techniques, including analogous, parametric, and bottom-up methods • Prepare a project plan using progressive elaboration and the Critical Path Method • Lead the project team and manage relationships effectively and productively across the spectrum of project stakeholders • Identify project risk and manage change • Evaluate and report on project performance Now completely revised and updated, Successful Project Management aligns with the 5thedition of A Guide to the Project Management Body of Knowledge (PMBOK® Guide), and covers outsourcing and procurement planning, the role of the Project Management Office, the use of three-point estimating and Monte Carlo simulation, earned value management, and much more. …”

“…Explore key ideas and strategies that underlie Bayesian analysis Distinguish prior, likelihood, and posterior distributions, and compare algorithms for driving sampling inputs Use grid approximation to solve simple univariate problems, and understand its limits as parameters increase Perform complex simulations and regressions with quadratic approximation and Richard McElreath's quap function Manage text values as if they were numeric Learn today's gold-standard Bayesian sampling technique: Markov Chain Monte Carlo (MCMC) Use MCMC to optimize execution speed in high-complexity problems Discover when frequentist methods fail and Bayesian methods are essential---and when to use both in tandem…”

“…Coverage includes Preparing your computer to run CUDA programs Understanding CUDA’s parallelism model and C extensions Transferring data between CPU and GPU Managing timing, profiling, error handling, and debugging Creating 2D grids Interoperating with OpenGL to provide real-time user interactivity Performing basic simulations with differential equations Using stencils to manage related computations across threads Exploiting CUDA’s shared memory capability to enhance performance Interacting with 3D data: slicing, volume rendering, and ray casting Using CUDA libraries Finding more CUDA resources and code Realistic example applications include Visualizing functions in 2D and 3D Solving differential equations while changing initial or boundary conditions Viewing/processing images or image stacks Computing inner products and centroids Solving systems of linear algebraic equations Monte-Carlo computations…”

“…Solve real business problems with Exceland build your competitive advantage: Quickly transition from Excel basics to sophisticated analytics Use PowerQuery or Get & Transform to connect, combine, and refine data sources Leverage Office 365's new Geography and Stock data types and six new functions Illuminate insights from geographic and temporal data with 3D Maps Summarize data with pivot tables, descriptive statistics, histograms, and Pareto charts Use Excel trend curves, multiple regression, and exponential smoothing Delve into key financial, statistical, and time functions Master all of Excel's great charts Quickly create forecasts from historical time-based data Use Solver to optimize product mix, logistics, work schedules, and investmentsand even rate sports teams Run Monte Carlo simulations on stock prices and bidding models Learn about basic probability and Bayes' Theorem Use the Data Model and Power Pivot to effectively build and use relational data sources inside an Excel workbook Automate repetitive analytics tasks by using macros…”

“…Minitab® is used to identify the best-fitting distributions from data sets for individual components. Monte Carlo Simulation with Crystal Ball® is then employed to determine the most important individual sources of variation and the overall variation of the final product. …”

“…Fully revised to incorporate valuation lessons learned from the last five years, from the market crisis and emerging markets to new types of equity investments Includes valuation practices across the life cycle of companies and emphasizes value enhancement measures, such as EVA and CFROI Contains a new chapter on probabilistic valuation techniques such as decision trees and Monte Carlo Simulation Author Aswath Damodaran is regarded as one of the best educators and thinkers on the topic of investment valuation This indispensable guide is a must read for anyone wishing to gain a better understanding of investment valuation and its methods. …”

“…Fully revised to incorporate valuation lessons learned from the last five years, from the market crisis and emerging markets to new types of equity investments Includes valuation practices across the life cycle of companies and emphasizes value enhancement measures, such as EVA and CFROI Contains a new chapter on probabilistic valuation techniques such as decision trees and Monte Carlo Simulation Author Aswath Damodaran is regarded as one of the best educators and thinkers on the topic of investment valuation This indispensable guide is a must read for anyone wishing to gain a better understanding of investment valuation and its methods. …”

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“…The appendix contains many useful fundamental formulae, as well as an explanation of the theoretical background for the direct simulation Monte Carlo (DSMC) method, easily accessible to nonmathematicians and not found elsewhere in the literature. …”

“…The Excel tips and techniques covered in this course fall into six categories: Productivity tips (navigation, flash fill, cell protection, advanced sorting & filtering, and more) Formatting tips (freeze panes, row/column grouping, advanced custom formats, and more) Formula tips (formula auditing tools, fuzzy lookups, detecting duplicates, randomization, and more) Visualization tips (sparklines, filled maps, custom templates, Excel form controls, and more) Pivot Table tips (slicers & timelines, custom layouts, value calculations, and more) Analytics tips (outlier detection, Monte Carlo simulation, forecasting, CUBE functions, and more) While the level of difficulty varies considerably, the demos generally start simple and become progressively more complex within each section. …”

“…This important resource: • Includes statistical analysis of engineering design parameters using Statistical Package for the Social Sciences (SPSS) • Presents Monte Carlos simulation using Crystal ball to quantify uncertainty and sensitivity of design parameters • Contains design methods of new energy, materials, processes, products, and system to achieve energy positive WRRF that are illustrated with Matlab • Provides information on life cycle costs in terms of capital and operation for different processes using MatLab Written for senior or graduates in environmental or chemical engineering, Sustainable Environmental Engineering defines and illustrates the TDPs of SE..…”