Lecture M: Final Exam Review (2019-12-03)

Review 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

This 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

This 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 covered in Part 1 of this lecture.

Lecture K1: Variance Reduction Techniques, Part 1 (2019-11-19) – CRN and Control Variates

This 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 primarily focuses on Common Random Numbers (CRN) and Control Variates.

Lecture J4: Estimation of Relative Performance (2019-11-14)

In 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 the scientific literature. We then cover how to generate confidence intervals for 2-sample tests and use those pairwise confidence intervals with other techniques to do ranking and selection of more than 2 models within a simulation study. Thus, the main focus of this lecture is methods for comparing and contrasting among two-to-many simulation models.

Lecture J3: Estimation of Absolute Performance, Part 3 - Steady-State Simulations (2019-11-12)

This 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 bias in steady-state simulation models of non-terminating systems, making use of intelligent initialization, warm-up periods, and batch means.

Lecture J2: Estimation of Absolute Performance, Part 2 - Transient Simulations (2019-11-07)

This 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)

This 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 the concepts of non-terminating systems (and their steady-state simulation models) and terminating systems (and their transient simulation models). Finally, how these ideas are implemented in Arena is demonstrated. The rest of the Arena demonstration will be continued in the next lecture.

Lecture I: Statistical Reflections (2019-10-31) – Halloween Themed

Discussion 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 closes with a discussion of the multiple comparisons problem (applied to simulation problems) and tools, such as Bonferroni correction, that can be used to prevent "statistical fishing."

Many Halloween-inspired examples are given during the lecture, and the relationship between ghost busting and statistics is exploited.

Lecture H: Output Verification, Validation, and Calibration (2019-10-29)

This 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 data from a real system. There is a period at the end of the lecture where I accidentally refer to an OC curve as plotting effect size versus statistical power. I meant to say that it plots effect size versus false negative rate (beta, or type-II error). This is fixed in the posted slides for the class.

Lecture G3: Input Modeling, Part 3 (2019-10-24)

Part 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 fits. It also includes a general discussion about hypothesis testing and cautionary notes about p-values.

Lecture G2: Input Modeling, Part 2 (2019-10-22)

Part 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 G1: Input Modeling, Part 1 (2019-10-10)

This lecture gives an overview of the process of input modeling and drills down into considerations that should be taken during the data collection process.

Lecture F: Midterm Review (2019-10-03)

This lecture is intended as a midterm review, but much of the content covered goes over inverse-transform sampling (both continuous and discrete).

During the lecture, questions were answered that involved whiteboard work. That whiteboard work was captured electronically in the two following images.

Lecture E2: Random-Variate Generation (2019-10-01)

In today's course, we revisited the tests of uniformity and independence necessary for random-number generation. We also started to formally introduce inverse-transform sampling. We will cover the discrete versions of inverse-transform sampling at the start of the next lecture.

Lecture E1: Random Number Generation (2019-09-26)

In this lecture, we go over methods for generating uniformly distributed random numbers and testing their uniformity and independence.

Lecture D2: Probabilistic Models (2019-09-24)

In this lecture, we review our motivation to build probabilistic models as input models for stochastic simulation. We then cover some basic probabilistic models that anyone working in stochastic simulation should be familiar with as options for basic input models.

Lecture D1: Probability and Random Variables (2019-09-17)

Today's lecture covers the basics of probability (including introduction to measure spaces) and random variables. We also go over some results from Lab 3.

Lecture C2: Introduction to Agent-Based Modeling and NetLogo (2019-09-17)

There is no podcast for this entry because of a cancelled class this week. Instead, students can watch this four-part NetLogo tutorial to help with this week's agent-based modeling lab, Lab 4.

Part 1: NetLogo Interface

Part 2: NetLogo Entities and Attributes

Part 3: Getting Started and Operational Contexts in NetLogo

Part 4: NetLogo Code Structure and Examples

Lecture C1: Basic Simulation Tools and Techniques (2019-09-12)

Today's lecture covered basic OR models that can be studied using a spreadsheet (simple queues, inventory management, and Monte Carlo simulation methodology). It hopefully provided motivation for using more sophisticated, specialized tools for modeling of more complex systems.

Lecture B3: Discrete Event System Simulation Examples II (2019-09-10)

The lecture today also included slides commenting on Lab 2, a hand-simulation that also allowed for discussion of the need for experimental replication and good statistical methods.

Lecture B2: Discrete Event System Simulation Examples I (2019-09-05)

Today, we cover the steps of hand-simulating a DES simulation model (which we missed in the previous lecture) and go over an example similar to the homework of hand simulating a single-server queueing system.

Lecture B1: Fundamental Concepts of DES Simulation (2019-09-03)

Due to unavoidable delays in arriving to class and some technical problems with the classroom equipment, this lecture is a little shorter than usual. We will pick up where we left off in Lecture B2 in two days.

Lecture A2: Introduction to Simulation Modeling (2019-08-29)

Lecture A1: Introduction to Modeling (2019-08-27)

Unfortunately, the video for this lecture cut off early. However, the audio continues through the whole lecture.