In 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 systems to make an inference about whether they differ). This allows us to discuss how confidence intervals are used in regression analysis and start to motivate how to build confidence intervals that are summarizes of 2-sample (instead of 1-sample) t-tests. We had to stop a little early, and so the next lecture will discuss how to convert paired t-tests and two different types of 2-sample t-tests into 2-sample confidence intervals (which are compared to 0).
Archived lectures from undergraduate course on stochastic simulation given at Arizona State University by Ted Pavlic
Tuesday, November 16, 2021
Lecture J4 (2021-11-16): Estimation of Relative Performance
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