Probability Density Function
Term · Environment · MLC-T-ENV-022214
1. Mathematical, graphical, or tabular expression of the relative likelihoods with which an unknown or variable quantity may take various values. The sum (or integral) of all likelihoods equals one for discrete (continuous) random variables. These distributions arise from the fundamental properties of the quantities we are attempting to represent. For example, quantities formed from adding many uncertain parameters tend to be normally distributed, and quantities formed from multiplying uncertain quantities tend to be lognormal.
2. Probability density functions are particularly useful in describing the relative likelihood that a variable will have different particular values of x. The probability that a variable will have a value within a small interval around x can be approximated by multiplying f(x) (i.e., the value of y at x in a PDF plot) by the width of the interval.
3. Mathematical, graphical, or tabular expression of the relative likelihoods with which an unknown or variable quantity may take various values. The sum (or integral) of all likelihoods equals one for discrete (continous) random variables. These distributions arise from the fundamental properties of the quantities we are attempting to represent. For example, quantities formed from adding many uncertain parameters tend to be normally distributed, and quantities formed from multiplying uncertain quantities tend to be lognormal.
4. Probability density functions are particularly useful in describing the relative likelihood that a variable will have different particular values of x. The probability that a variable will have a value within a small interval around x can be approximated by multiplying f(x) (i.e., the value of y at x in a PDF plot) by the width of the interval. [EPA 1998a]
5. The probability density function of a continuous random variable is a function that can be integrated to obtain the probability that the random variable takes a value in a given interval. [STEPS 1997]
| Identifier | MLC-T-ENV-022214 |
|---|---|
| Field | Environment |
| Subject | Policy and guidance |
| Abbreviation | |
| References | Modeling Glossary; Waste and Cleanup Risk Assessment Glossary; EPA 1998a; Thesaurus of Terms Used in Microbial Risk Assessment |
Record as JSON
{
"id": "MLC-T-ENV-022214",
"term": "Probability Density Function",
"field": "Environment",
"definition": "1. Mathematical, graphical, or tabular expression of the relative likelihoods with which an unknown or variable quantity may take various values. The sum (or integral) of all likelihoods equals one for discrete (continuous) random variables. These distributions arise from the fundamental properties of the quantities we are attempting to represent. For example, quantities formed from adding many uncertain parameters tend to be normally distributed, and quantities formed from multiplying uncertain quantities tend to be lognormal.\n\n2. Probability density functions are particularly useful in describing the relative likelihood that a variable will have different particular values of x. The probability that a variable will have a value within a small interval around x can be approximated by multiplying f(x) (i.e., the value of y at x in a PDF plot) by the width of the interval.\n\n3. Mathematical, graphical, or tabular expression of the relative likelihoods with which an unknown or variable quantity may take various values. The sum (or integral) of all likelihoods equals one for discrete (continous) random variables. These distributions arise from the fundamental properties of the quantities we are attempting to represent. For example, quantities formed from adding many uncertain parameters tend to be normally distributed, and quantities formed from multiplying uncertain quantities tend to be lognormal.\n\n4. Probability density functions are particularly useful in describing the relative likelihood that a variable will have different particular values of x. The probability that a variable will have a value within a small interval around x can be approximated by multiplying f(x) (i.e., the value of y at x in a PDF plot) by the width of the interval. [EPA 1998a]\n\n5. The probability density function of a continuous random variable is a function that can be integrated to obtain the probability that the random variable takes a value in a given interval. [STEPS 1997]",
"abbreviation": "PDF",
"subject": "Policy and guidance",
"references": [
"Modeling Glossary",
"Waste and Cleanup Risk Assessment Glossary; EPA 1998a",
"Thesaurus of Terms Used in Microbial Risk Assessment"
],
"url": "https://mlchart.com/terminology/environment/probability-density-function/"
}
Record 22,214 of 30,736 in Environment terminology (MLC-0121). Request the full dataset.