In this lecture, we review basic probability space concepts from the previous lecture. We then go on to discuss the common probabilistic models that we will use in stochastic simulation (e.g., uniform, triangular, normal, exponential, Weibull, Erlang, Poisson, etc.). Basic background on the structure of each distribution is given as well as practical reasons why one distribution might be picked over another.
Subscribe to:
Post Comments (Atom)
Popular Posts
-
This lecture introduces students to IEE 475 (Simulating Stochastic Systems), a required course for Industrial Engineering majors that covers...
-
In this lecture, we introduce the measure-theoretic concept of a random variable (which is neither random nor a variable) and related terms,...
-
This lecture covers content related to implementing simulations with spreadsheets and the motivations for the use of special-purpose Discret...
-
In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete eve...
-
This lecture provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other...
-
In this lecture, we cover fundamentals of discrete-event system (DES) simulation (DESS). This involves reviewing basic simulation concepts (...
-
During this lecture, we review the topics covered up to this point in the course as preparation for the upcoming midterm exam. Students are ...
-
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 first cover some discrete distributions (and the Poisson process) that we ran out of time for during the previous lectur...
-
In this lecture, we review statistical fundamentals – such as the origins of the t-test, the meaning of type-I and type-II error (and alter...
No comments:
Post a Comment