Latest Discrete Podcast Episodes
Episode 1: From P60 | 3 19 2023
Savvy Records Podcast - March 23, 2023 00:00 - 58 minuteswww.soundcloud.com/from-p60 www.instagram.com/fromp60 www.facebook.com/fromp60
Lecture M (2022-11-29): Final Exam Review
IEE 475: Simulating Stochastic Systems - November 29, 2022 22:43In this lecture, we prepare for the final exam and give a brief review of all topics from the course.
Lecture L (2022-11-22): Course Wrap Up
IEE 475: Simulating Stochastic Systems - November 23, 2022 00:57In this lecture, we wrap up the course content in IEE 475. We first do a quick overview of the four variance reduction techniques (VRT's) covered in the previous unit. That is, we cover: common random numbers (CRN's), antithetic variates (AV's), importance sampling, and control variates. We then...
Lecture K1 (2022-11-15): Variance Reduction Techniques, Part 1 (CRNs and Control Variates)
IEE 475: Simulating Stochastic Systems - November 16, 2022 19:42In this lecture, we start by reviewing approaches for absolute and relative performance estimation in stochastic simulation. This begins with a reminder of the use of confidence intervals for estimation of performance for a single simulation model. We then move to different ways to use confidenc...
Lecture J4 (2022-11-10): Estimation of Relative Performance
IEE 475: Simulating Stochastic Systems - November 10, 2022 23:17In this lecture, we review what we have learned about one-sample confidence intervals (i.e., how to use them as graphical versions of one-sample t-tests) for absolute performance estimation in order to motivate the problem of relative performance estimation. We introduce two-sample confidence in...
Lecture J3 (2022-11-08): Estimation of Absolute Performance, Part III (Non-Terminating Systems/Steady-State Simulations)
IEE 475: Simulating Stochastic Systems - November 08, 2022 23:02In this lecture, we start by further reviewing confidence intervals (where they come from and what they mean) and prediction intervals and then use them to motivate a simpler way to determine how many replications are needed in a simulation study (focusing first on transient simulations of termi...
Lecture J2 (2022-11-03): Estimation of Absolute Performance, Part II (Terminating Systems/Transient Simulations)
IEE 475: Simulating Stochastic Systems - November 04, 2022 20:29In this lecture, we review estimating absolute performance from simulation, with focus on choosing the number of necessary replications of transient simulations of terminating systems. The lecture starts by overviewing point estimation, bias, and different types of point estimators. This include...
Lecture J1 (2022-11-01): Estimation of Absolute Performance, Part 1 (Introduction to Point and Interval Estimation)
IEE 475: Simulating Stochastic Systems - November 02, 2022 19:18In this lecture, we introduce the estimation of absolute performance measures in simulation – effectively shifting our focus from validating input models to validating and making inferences about simulation outputs. Most of this lecture is a review of statistics and reasons for the assumptions f...
Lecture I (2022-10-27): Statistical Reflections [Halloween Themed]
IEE 475: Simulating Stochastic Systems - October 28, 2022 17:52In this lecture, we review statistical fundamentals – such as the origins of the t-test, the meaning of type-I and type-II error (and alternative terminology for both, such as false positive rate and false negative rate) and the connection to statistical power (sensitivity). We review the Receiv...
Lecture H (2022-10-25): Verification, Validation, and Calibration of Simulation Models (plus some Lecture G3 slides)
IEE 475: Simulating Stochastic Systems - October 25, 2022 23:08In this lecture, we mostly cover slides from Lecture G3 (on goodness of fit) that were missed during the previous lecture. In particular, we review hypothesis testing fundamentals (type-I error, type-II error, statistical power, sensitivity, false positive rate, true negative rate, receiver oper...
Lecture G3 (2022-10-20): Input Modeling, Part 3 (Parameter Estimation and Goodness of Fit)
IEE 475: Simulating Stochastic Systems - October 20, 2022 23:00In this lecture, we (nearly) finish our coverage of Input Modeling, where the focus of this lecture is on parameter estimation and assessing goodness of fit. We review input modeling in general and then briefly review fundamentals of hypothesis testing. We discuss type-I error, p-values, type-II...
Lecture G2 (2022-10-18): Input Modeling, Part 2 (Selection of Model Structure)
IEE 475: Simulating Stochastic Systems - October 18, 2022 22:39In this lecture, we continue discussing the choice of input models in stochastic simulation. Here, we pivot from talking about data collection to selection of the broad family of probabilistic distributions that may be a good fit for data. We start with an example where a histogram leads us to i...
Lecture G1 (2022-10-13): Input Modeling, Part 1 (Data Collection)
IEE 475: Simulating Stochastic Systems - October 14, 2022 17:53In this lecture, we introduce the detailed process of input modeling. Input models are probabilistic models that introduce variation in simulation models of systems. Those input models must be chosen to match statistical distributions in data. Over this unit, we cover collection of data for this...
Lecture F (2022-09-29): Midterm Review
IEE 475: Simulating Stochastic Systems - September 30, 2022 01:10Midterm review session for ASU IEE 475 for Fall 2022. Whiteboard notes for this lecture can be found at: https://www.dropbox.com/s/ljc61rarhns41u2/2022-Fall-Midterm_Review_Notes.pdf?dl=0
Lecture E2 (2022-09-27): Random-Variate Generation
IEE 475: Simulating Stochastic Systems - September 28, 2022 04:46In this lecture, we review pseudo-random number generation and then introduce random-variate generation by way of inverse-transform sampling. In particular, we start with a review of the two most important properties of a pseudo-random number generator (PRNG), uniformity and independence, and di...
Lecture E1 (2022-09-22): Random-Number Generation
IEE 475: Simulating Stochastic Systems - September 22, 2022 23:14In this lecture, we first cover some discrete distributions (and the Poisson process) that we ran out of time for during the previous lecture. We then launch into a discussion of how to generate pseudo-random numbers distributed uniformly between 0 and 1 (which are necessary for us to easily gen...
Lecture D2 (2022-09-20): Probabilistic Models
IEE 475: Simulating Stochastic Systems - September 20, 2022 23:02In this lecture, we review basic probability fundamentals (measure spaces, probability measures, random variables, probability density functions, probability mass functions, cumulative distribution functions, moments, mean/expected value/center of mass, standard deviation, variance), and then we...
Lecture D1 (2022-09-15): Probability and Random Variables
IEE 475: Simulating Stochastic Systems - September 15, 2022 23:05In this lecture, we introduce the measure-theoretic concept of a random variable (which is neither random nor a variable) and related terms, such as outcomes, events, probability measures, moments, means, etc. Throughout the lecture, we use the metaphor of probability as mass (and thus probabili...
Lecture C2 (2022-09-13): Beyond DES Simulation – SDM, ABM, and NetLogo (and pre-lab discussion for Lab 4 and post-lab discussion for Lab 3)
IEE 475: Simulating Stochastic Systems - September 14, 2022 04:12In this lecture, we briefly introduce System Dynamics Modeling (SDM) and Agent-Based/Individual-Based Modeling (ABM/IBM) as the two ends of the simulation modeling spectrum (from low resolution to high resolution). The introduction of ABM describes applications in life sciences, social sciences,...
Lecture C1 (2022-09-08): Basic Simulation Tools and Techniques
IEE 475: Simulating Stochastic Systems - September 08, 2022 22:57In this lecture, we discuss different approaches to implementing Discrete Event System (DES) simulations (DESS) with simple spreadsheets (e.g., Microsoft Excel, Google Sheets, Apple Numbers, etc.). We cover inventory management problems (such as the newsvendor model) as well as Monte Carlo sampl...
Lecture B3 (2022-09-06): DES Examples, Part II, plus Post-Lab2 Reflections
IEE 475: Simulating Stochastic Systems - September 06, 2022 22:38In this lecture, we close out our review of DES fundamentals and hand simulation. After going through a hand-simulation example one last time, we show how to implement a Discrete Event System (DES) simulation using a spreadsheet tool like Microsoft Excel without any "macros" (VBA, etc.). This in...
Lecture B2 (2022-09-01): DES Examples, Part I
IEE 475: Simulating Stochastic Systems - September 01, 2022 22:54In this lecture, we review fundamentals of Discrete Event System (DES) simulation (e.g., entities, resources, activities, processes, delays, attributes) and we run through a number of DES modeling examples. These examples show how different research/operations questions can lead to different cho...
Lecture B1 (2022-08-30): Fundamental Concepts of Discrete-Event Simulation
IEE 475: Simulating Stochastic Systems - August 30, 2022 22:56In this lecture, we cover fundamentals of discrete-event system (DES) simulation (DESS). This involves reviewing basic simulation concepts (entities, resources, attributes, events, activities, delays) and introducing the event-scheduling world view, which provides a causality framework on which ...
Lecture A2 (2022-08-25): Introduction to Simulation Modeling
IEE 475: Simulating Stochastic Systems - August 25, 2022 23:13In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete event system simulation) and then focus on how stochastic modeling is used within discrete-event system simulation.
Lecture A1 (2022-08-23): Introduction to Modeling
IEE 475: Simulating Stochastic Systems - August 23, 2022 22:39In this lecture, we introduce Industrial and Systems Engineering as a blend of science and engineering that necessitates model building. We then define model (as something that answers a "What If" question) and different types of models. This gives us an opportunity to discuss how modeling is le...
Lecture 0 (2022-08-18): Course Introduction
IEE 475: Simulating Stochastic Systems - August 18, 2022 22:02In this lecture, we go over course policies for the Fall 2022 session of IEE 475.
Lecture M (2021-11-30): Final Exam Review [re-post of Fall 2020 Lecture M on 2020-12-01]
IEE 475: Simulating Stochastic Systems - November 30, 2021 20:36This lecture section is a cumulative review of material from the semester and is meant to serve as a study guide for students preparing for the upcoming final exam. Topics start at modeling fundamentals (what is the purpose of a model in general) to the specifics of designing statistical experim...
Lecture K2 (2021-11-23): Variance Reduction Techniques, Part 2
IEE 475: Simulating Stochastic Systems - November 23, 2021 21:33In this lecture, we wrap up our discussion of Variance Reduction Techniques. We introduced Common Random Numbers (CRNs) last time, which we review in this lecture. We then introduce Control Variates (CVs), Antithetic Variates (AVs), and Importance Sampling. These four methods are all examples of...
Lecture K1 (2021-11-18): Variance Reduction Techniques, Part 1
IEE 475: Simulating Stochastic Systems - November 18, 2021 20:10In this lecture, we wrap up our discussion of the movement from point estimation (sample means) to interval estimation for: (a) estimating absolute performance of a system, (b) estimating relative performance of two systems, and (c) estimating relative performance of more than 2 systems. We then...
Lecture J4 (2021-11-16): Estimation of Relative Performance
IEE 475: Simulating Stochastic Systems - November 16, 2021 20:25In this lecture, we further review the use of confidence intervals to summarize empirical results from simulation as we move from thinking about absolute performance estimation (i.e., using one model system to estimate one parameter) to relative performance estimation (i.e., comparing two model ...
Related Discrete Topics
- Simulation 3
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