 Package org.sbml.jsbml.ext.distrib

## Enum UncertParameter.Type

• java.lang.Object
• ### Enum Constant Summary

Enum Constants
Enum Constant Description
`coefficientOfVariation`
For a random variable with mean μ and strictly positive standard deviation σ, the coefficient of variation is defined as the ratio (σ / |μ|).One benefit of using the coefficient of variation rather than the standard deviation is that it is unitless.
`confidenceInterval`
For a univariate random variable x, a confidence interval is a range [a, b], a < b, so that x lies between a and b with given probability.
`credibleInterval`
In Bayesian statistics, a credible interval is similar to a confidence interval determined from the posterior distribution of a random variable x.
`distribution`
If the uncertainty is defined by a known distribution, that distribution may either be defined by using the child math element, or by using the definitionURL .
`externalParameter`
This type is uniquely described by an appropriate definitionURL, and is provided to allow a modeler to encode externally-provided parameters not otherwise explicitly handled by this specification.
`interquartileRange`
The interquartile range is the range between the 1st and 3rd quartiles.
`kurtosis`
The kurtosis of a distribution is a measure of how peaked the distribution is.
`mean`
The arithmetic mean (typically just the mean) is what is commonly called the average.
`median`
The median is described as the numeric value separating the higher half of a sample (or population) from the lower half.
`mode`
The mode is the value that occurs the most frequently in a data set (or a probability distribution).
`range`
The range is the interval [a, b] so that a < b and contains all possible values of x.
`sampleSize`
The sample size is a direct count of the number of observations made or the number of samples measured.
`skewness`
The skewness of a random variable is a measure of how asymmetric the corresponding probability distribution is.
`standardDeviation`
The standard deviation of a distribution or population is the square root of its variance.
`standardError`
The standard error is the standard deviation of estimates of a population value.
`variance`
The variance of a random quantity (or distribution) is the average value of the square of the deviation of that variable from its mean.
• ### Method Summary

All Methods
Modifier and Type Method Description
`static UncertParameter.Type` `valueOf​(java.lang.String name)`
Returns the enum constant of this type with the specified name.
`static UncertParameter.Type[]` `values​()`
Returns an array containing the constants of this enum type, in the order they are declared.
• ### Methods inherited from class java.lang.Enum

`compareTo, equals, getDeclaringClass, hashCode, name, ordinal, toString, valueOf`
• ### Methods inherited from class java.lang.Object

`getClass, notify, notifyAll, wait, wait, wait`
• ### Enum Constant Detail

• #### coefficientOfVariation

`public static final UncertParameter.Type coefficientOfVariation`
For a random variable with mean μ and strictly positive standard deviation σ, the coefficient of variation is defined as the ratio (σ / |μ|).One benefit of using the coefficient of variation rather than the standard deviation is that it is unitless.
• #### kurtosis

`public static final UncertParameter.Type kurtosis`
The kurtosis of a distribution is a measure of how peaked the distribution is.
• #### mean

`public static final UncertParameter.Type mean`
The arithmetic mean (typically just the mean) is what is commonly called the average.
• #### median

`public static final UncertParameter.Type median`
The median is described as the numeric value separating the higher half of a sample (or population) from the lower half. The median of a finite list of numbers can be found by arranging all the observations from lowest value to highest value and picking the middle one. If there is an even number of observations, then there is no single middle value, then the average of the two middle values is used. The median is also the 0.5 quantile, or 50th percentile.
• #### mode

`public static final UncertParameter.Type mode`
The mode is the value that occurs the most frequently in a data set (or a probability distribution). It need not be unique (e.g., two or more quantities occur equally often) and is typically defined for continuous valued quantities by first defining the histogram, and then giving the central value of the bin containing the most counts.
• #### sampleSize

`public static final UncertParameter.Type sampleSize`
The sample size is a direct count of the number of observations made or the number of samples measured. It is used in several other statistical measurements, and can be used to convert one to another.
• #### skewness

`public static final UncertParameter.Type skewness`
The skewness of a random variable is a measure of how asymmetric the corresponding probability distribution is.
• #### standardDeviation

`public static final UncertParameter.Type standardDeviation`
The standard deviation of a distribution or population is the square root of its variance. It is a widely used measure of the variability or dispersion since it is reported in the natural units of the quantity being considered. Note that if a finite sample of a population has been used then the sample standard deviation is the appropriate unbiased estimator to use.
• #### standardError

`public static final UncertParameter.Type standardError`
The standard error is the standard deviation of estimates of a population value. If that population value is a mean, this statistic is called the standard error of the mean. It is calculated as the standard deviation of a sample divided by the square root of the number of the sample size. As the sample size increases, the sample size draws closer to the population size, and the standard error approaches zero.
• #### variance

`public static final UncertParameter.Type variance`
The variance of a random quantity (or distribution) is the average value of the square of the deviation of that variable from its mean.
• #### confidenceInterval

`public static final UncertParameter.Type confidenceInterval`
For a univariate random variable x, a confidence interval is a range [a, b], a < b, so that x lies between a and b with given probability. For example, a 95% confidence interval is a range in which x falls 95% of the time (or with probability 0.95). Confidence intervals provide intuitive summaries of the statistics of the variable x.

Unless specified otherwise, the confidence interval is usually chosen so that the remaining probability is split equally, that is P (x < a) = P (x > b). If x has a symmetric distribution, then the confidence intervals are usually centered around the mean. However, non-centered confidence intervals are possible and are better described by their lower and upper quantiles or levels. For example, a 50% confidence interval would usually lie between the 25% and 75% quantiles, but could in theory also lie between the 10% and 60% quantiles, although this would be rare in practice. The confidenceInterval allows you the flexibility to specify non-symmetric confidence intervals however in practice we would expect the main usage to be for symmetric intervals.

The confidenceInterval child of a Uncertainty is always the 95% confidence interval. For other confidence intervals, use an UncertParameter of type 'externalParameter' instead.

• #### credibleInterval

`public static final UncertParameter.Type credibleInterval`
In Bayesian statistics, a credible interval is similar to a confidence interval determined from the posterior distribution of a random variable x. That is, given a prior distribution p(x) and some observations D, the posterior probability p(x | D) can be computed using Bayes theorem. Note that the interpretation of a credible interval is not the same as a (frequentist) confidence interval.

The credibleInterval child of a Uncertainty is always the 95% credible interval. For other credibility intervals, use an UncertParameter of type 'externalParameter' instead.

• #### interquartileRange

`public static final UncertParameter.Type interquartileRange`
The interquartile range is the range between the 1st and 3rd quartiles. It contains the middle 50% of the sample realisations (or of the sample probability). It is typically used and shown in box plots.
• #### range

`public static final UncertParameter.Type range`
The range is the interval [a, b] so that a < b and contains all possible values of x. This is also often called the statistical range, which is the distance from the smallest value to the largest value in a sample dataset. For a sample dataset X = (x 1 , ..., x N ), the range is the distance from the smallest x i to the largest. It is often used as a first estimate of the sample dispersion.
• #### distribution

`public static final UncertParameter.Type distribution`
If the uncertainty is defined by a known distribution, that distribution may either be defined by using the child math element, or by using the definitionURL . When the math child is used, that math should contain the distribution in question: typically this will be a distribution csymbol but may be something more complicated, like a piecewise function. If the definitionURL is used, many more distributions may be used than are defined in this specification (like an externalParameter , below). To fully define this distribution, it will almost certainly be necessary to further define that distribution with child UncertParameter elements. For example, a Beta distribution takes two parameters (α and β), each of which could be defined by a child UncertParameter of type 'externalParameter', with appropriate definitionURL values. A type of value 'distribution' is only valid for UncertParameter elements, not UncertSpan elements.
• #### externalParameter

`public static final UncertParameter.Type externalParameter`
This type is uniquely described by an appropriate definitionURL, and is provided to allow a modeler to encode externally-provided parameters not otherwise explicitly handled by this specification. The range of possibilities is vast, so modelers should ensure that the tool they wish to use encodes support for any UncertParameter they define. As an external parameter may take any form, there are no restrictions on what other attributes or children may be used by an UncertParameter of this type: it may be a single value; it may be a span; it may be defined by a child math element; it may be defined by child UncertParameter elements; it may be defined by any combination of the above. The only restriction is that the definitionURL must be defined for any UncertParameter of type 'externalParameter'. This type value may be used for either UncertParameter or UncertSpan elements.
• ### Method Detail

• #### values

`public static UncertParameter.Type[] values​()`
Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:
```for (UncertParameter.Type c : UncertParameter.Type.values())
System.out.println(c);
```
Returns:
an array containing the constants of this enum type, in the order they are declared
• #### valueOf

`public static UncertParameter.Type valueOf​(java.lang.String name)`
Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)
Parameters:
`name` - the name of the enum constant to be returned.
Returns:
the enum constant with the specified name
Throws:
`java.lang.IllegalArgumentException` - if this enum type has no constant with the specified name
`java.lang.NullPointerException` - if the argument is null