Latest Discrete Podcast Episodes
Lecture G3 (2020-10-22): Input Modeling, Part 3
IEE 475: Simulating Stochastic Systems - October 22, 2020 20:15In this lecture, we continue our discussion of statistically rigorous methods for input modeling in simulation of stochastic systems. We first cover the basics of hypothesis testing, including a review of type-I error (alpha), p-values, and how they relate to critical values for goodness-of-fit ...
Lecture G2 (2020-10-20): Input Modeling, Part 2
IEE 475: Simulating Stochastic Systems - October 21, 2020 03:31This is the second part in a unit on input modeling for simulating stochastic systems (stochastic simulation). In the this part, we describe how to start making sense of data collected from real-world systems. We start with an example that builds a model of a single-server, single-channel queue ...
Lecture F2 (2020-10-13): Review Before Midterm Retake
IEE 475: Simulating Stochastic Systems - October 14, 2020 00:03In this lecture period, we discuss student-generated questions as a means of reviewing for the upcoming midterm retake. Most of the discussion centers around the solution set from the first midterm.
Lecture G1 (2020-10-08): Input Modeling, Part 1
IEE 475: Simulating Stochastic Systems - October 09, 2020 00:00In this lecture, we re-motivate the topic of Input Modeling in stochastic simulation. Input modeling is the process of choosing probabilistic models to represent realistic variation that is statistically similar to measured data even though the probabilistic models leave out the real-world detai...
Lecture F (2020-10-01): Midterm Review
IEE 475: Simulating Stochastic Systems - October 02, 2020 04:15Midterm review lecture for Fall 2020. Starts with an introduction to inverse-transform sampling for discrete random variables (including sampling from empirical CDF's).
Lecture E2 (2020-09-29): Random-Variate Generation
IEE 475: Simulating Stochastic Systems - September 30, 2020 03:18In this lecture, we review random-number generation and tests of uniformity and independence. We then focus on random-variate generation (for stochastic simulation) using inverse-transform sampling.
Lecture E1 (2020-09-24): Random Number Generation
IEE 475: Simulating Stochastic Systems - September 24, 2020 06:20This lecture surrounds random number generation. The topic is motivated by the need for generating samples from arbitrary random variables, which can be accomplished through transforming random numbers uniformly distributed between 0 and 1. We describe the key properties of a good pseudo-random ...
Lecture D2 (2020-09-22): Probabilistic Models
IEE 475: Simulating Stochastic Systems - September 22, 2020 22:58In this lecture, we review the motivations for stochastic modeling in discrete event system simulation. We also review the basics of probability theory (specifically probability spaces, random variables, probability density functions, probability mass functions, cumulative distribution functions...
Lecture D1 (2020-09-17): Probability and Random Variables
IEE 475: Simulating Stochastic Systems - September 17, 2020 23:34In this lecture, we introduce basic concepts from probability theory that will be useful as we move toward input modeling for Discrete Event System simulation modeling. Our introduction starts with a brief acknowledgment of measure theory and then a definition of random variables, sample spaces,...
Lecture C2 (2020-09-15): Beyond DES Simulation – SDM, ABM, and NetLogo (plus post-Lab3 discussion)
IEE 475: Simulating Stochastic Systems - September 15, 2020 22:35In this lecture, we first discussion results from Lab 3 on Monte Carlo simulations. Then we transition to motivate other forms of simulation outside of Discrete Event System simulation, such as System Dynamics Modeling and Agent-Based Modeling. This allows for introducing NetLogo, which is the s...
Lecture C1 (2020-09-10): Basic Simulation Tools and Techniques
IEE 475: Simulating Stochastic Systems - September 11, 2020 00:35In this lecture, we describe how simple computational tools such as spreadsheets can be be used not only for Discrete Event System simulation but also for Monte Carlo simulation. This provides an opportunity to describe different classes of inventory problems (newsvendor, order-up-to, etc.) that...
Lecture B3 (2020-09-08): DES Examples II (and post-lab discussion for Lab 2)
IEE 475: Simulating Stochastic Systems - September 08, 2020 23:21In this lecture, we first spend some time to review the results of Lab 2 – a lab focused on the hand simulation of an inventory-management problem. That provides a discussion of statistical blocking and common random numbers and how they are related to paired t-tests. It also provides a brief mo...
Lecture B2 (2020-09-03): DES Examples I
IEE 475: Simulating Stochastic Systems - September 03, 2020 22:19This lecture continues our introduction to Discrete Event System simulation (DESS). We cover examples of different ways to choose entities, resources, and activities for systems based upon the operations research question of interest. We then further describe the process of hand simulating a DES...
Lecture B1 (2020-09-01): Fundamentals of Discrete-Event Simulation
IEE 475: Simulating Stochastic Systems - September 02, 2020 00:05In this lecture, we continue the introduction to Discrete Event System (DES) simulation fundamentals. This includes revisiting the differences between entities, attributes, resources, state vectors, and output metrics. We discuss how "stochastic" modeling uses randomness to simplify building sim...
Lecture A2 (2020-08-27): Introduction to Simulation Modeling
IEE 475: Simulating Stochastic Systems - August 27, 2020 21:18In this lecture, we introduce the three different kinds of simulation modeling (system dynamics modeling, agent-based modeling, and discrete event system simulation) and how they differ in the kinds of questions they help answer, the way they are programmed, and the computational resources that ...
Lecture A1 (2020-08-25) - Introduction to Modeling
IEE 475: Simulating Stochastic Systems - August 25, 2020 22:45This lecture provides an introduction to modeling and how simulation is used within industrial engineering and operations research to gain insights into complex socio-technological systems.
Lecture 0 (2020-08-20): Introduction to the Course and Its Policies
IEE 475: Simulating Stochastic Systems - August 20, 2020 23:25Introduction to the course and the course policies that will be used in the Fall 2020 semester.
Lecture M: Final Exam Review (2019-12-03)
IEE 475: Simulating Stochastic Systems - December 03, 2019 21:12Review lecture to help students prepare for final exam. Covers all topics in this undergraduate stochastic simulation course.
Lecture L: Course Wrap Up (2019-11-26) – VRT Summary and Closing Course Comments
IEE 475: Simulating Stochastic Systems - November 26, 2019 20:38This lecture opens with a visual summary of four popular variance reduction techniques (VRTs), namely: common random numbers (CRN), antithetic variates (AV), importance sampling, and control variates. It then closes with a few concluding remarks about the IEE 475 course.
Lecture K2: Variance Reduction Techniques, Part 2 (2019-11-21) – Antithetic Variates and Importance Sampling
IEE 475: Simulating Stochastic Systems - November 21, 2019 20:17This lecture covers Variance Reduction Techniques (VRT) for stochastic simulation, covering: Common Random Numbers (CRNs), Control Variates (CVs), Antithetic Variates (AVs), and Importance Sampling. The lecture mainly focuses on AVs and Importance sampling with an overview of CRN and CV topics co...
Lecture K1: Variance Reduction Techniques, Part 1 (2019-11-19) – CRN and Control Variates
IEE 475: Simulating Stochastic Systems - November 20, 2019 00:27This lecture introduces the use of Variance Reduction Techniques (VRT), which combine tools from statistical experiment design with the idiosyncrasies of stochastic simulation studies to reduce the number of replications needed to make inferences by controlling sources of variance. This lecture p...
Lecture J4: Estimation of Relative Performance (2019-11-14)
IEE 475: Simulating Stochastic Systems - November 14, 2019 21:54In this lecture, we solidify our geometric understanding of a confidence interval and further reinforce why interval estimation should always be preferred over point estimates. Some linear regression examples (with confidence intervals on regression coefficients) are demonstrated using data from ...
Lecture J3: Estimation of Absolute Performance, Part 3 - Steady-State Simulations (2019-11-12)
IEE 475: Simulating Stochastic Systems - November 12, 2019 20:43This lecture stresses the importance of interval estimation over point estimation and demonstrates both how to interpret interval estimates as well as how the size of the intervals will change with sampling and variance parameters. It then concludes with discussion of how to avoid initialization ...
Lecture J2: Estimation of Absolute Performance, Part 2 - Transient Simulations (2019-11-07)
IEE 475: Simulating Stochastic Systems - November 12, 2019 20:39This lecture reviews content from Lecture J1 on point estimators, estimator bias, interval estimation of means and quantiles, and the relationship between confidence intervals on means and t-tests. It also gives an introduction to data output facilities in Arena for stochastic simulation.
Lecture J1: Estimation of Absolute Performance, Part 1 (2019-11-05)
IEE 475: Simulating Stochastic Systems - November 05, 2019 20:17This lecture re-hashes the rationale behind the Student's t-test, the Chi-squared test, and methods in hypothesis testing in general (both parametric and non-parametric). It then discusses issues in taking data both across simulation replications and within simulation replications and introduces ...
Lecture I: Statistical Reflections (2019-10-31) – Halloween Themed
IEE 475: Simulating Stochastic Systems - November 01, 2019 00:13Discussion of error rates and statistical power in hypothesis testing, along with a deeper investigation behind how the Student's t-test and the Chi-square test work and why they require the assumptions they do. An example paired-difference t-test with power analysis is done, and then lecture clo...
Lecture G3: Input Modeling, Part 3 (2019-10-24)
IEE 475: Simulating Stochastic Systems - October 29, 2019 20:31Part 3 of a 3-part lecture series on input modeling for stochastic simulation. This lecture describes point estimation of parameters by maximum likelihood as well as the use of goodness-of-fit techniques (Chi-square, Kolmogorov-Smirnov, Anderson-Darling, and Shapiro-Wilk) to evaluate those best f...
Lecture H: Output Verification, Validation, and Calibration (2019-10-29)
IEE 475: Simulating Stochastic Systems - October 29, 2019 20:30This lecture covers testing, validation, verification, and the general process of simulation model calibration. Specific quantitative topics involve power analysis of a one-sample, two-tailed t-test as well as the application of a paired t-test for analyzing validity of a simulation model using d...
Lecture G2: Input Modeling, Part 2 (2019-10-22)
IEE 475: Simulating Stochastic Systems - October 22, 2019 20:22Part 2 of a 3-part lecture series on input modeling for stochastic simulation. This lecture describes going from data to coarse logic flows and then using tools like probability plots to choose distributions for elements of those flows.
Lecture E1: Random Number Generation (2019-09-26)
IEE 475: Simulating Stochastic Systems - October 20, 2019 01:54In this lecture, we go over methods for generating uniformly distributed random numbers and testing their uniformity and independence.
Related Discrete Topics
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