Choosing the Probability Distribution from Data

Nick T. Thomopoulos
Additional contact information
Nick T. Thomopoulos: Illinois Institute of Technology, Stuart School of Business

Chapter Chapter 10 in Essentials of Monte Carlo Simulation, 2013, pp 113-135 from Springer

Abstract: Abstract Computer simulation models often include one or more variables that play important roles in the model. Some of the random variables are of the continuous type and others are discrete. The analyst is confronted with choosing the proper probability distribution for each variable, and also with estimating the associated parameter(s) value. The chapter describes some of the common ways to select the distribution and to estimate the associated parameter values when some empirical or sample data is available from the real system.

Keywords: Probability Distribution; Sample Average; Cumulative Probability Distribution; Discrete Probability Distribution; Empirical Cumulative Distribution Function (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-6022-0_10

Ordering information: This item can be ordered from
http://www.springer.com/9781461460220

DOI: 10.1007/978-1-4614-6022-0_10

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().