In 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 process, choice of probabilistic families to fit to these data, and then optimized parameter choice within those families and evaluation of fit with goodness of fit. In this lecture, we discuss issues related to data collection.
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Friday, October 18, 2024
Lecture G1 (2024-10-17): Input Modeling, Part 1: Data Collection
Labels:
podcast
Location:
Tempe, AZ, USA
Subscribe to:
Post Comments (Atom)
Popular Posts
-
In this lecture, we close out our review of DES fundamentals and hand simulation. After going through a hand-simulation example one last tim...
-
This lecture covers Variance Reduction Techniques (VRT) for stochastic simulation, covering: Common Random Numbers (CRNs), Control Variates ...
-
In this lecture, we review basic probability space concepts from the previous lecture. We then go on to discuss the common probabilistic mod...
-
In this lecture, we review topics from the first half of the semester that will be tested over in the upcoming midterm. Most of the class in...
-
In this lecture, we introduce the detailed process of input modeling. Input models are probabilistic models that introduce variation in simu...
-
In this lecture, we review pseudo-random number generation and then introduce random-variate generation by way of inverse-transform sampling...
-
In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete eve...
-
In this lecture, we (nearly) finish our coverage of Input Modeling, where the focus of this lecture is on parameter estimation and assessing...
-
In this lecture, we continue to discuss hypothesis testing -- introducing parametric, non-parametric, exact, and non-exact tests and reviewi...
-
In 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...
No comments:
Post a Comment