histogram_bin_uncertain
Aleatory uncertain variable  continuous histogram
Topics
continuous_variables, aleatory_uncertain_variables
Specification
Alias: None
Arguments: INTEGER
Default: no histogram bin uncertain variables
Child Keywords:
Required/Optional 
Description of Group 
Dakota Keyword 
Dakota Keyword Description 

Optional 
Number of pairs defining each histogram bin variable 

Required 
Real abscissas for a bin histogram 

Required (Choose One) 
Density Values 
Ordinates specifying a “skyline” probability density function 

Frequency or relative probability of each bin 

Optional 
Initial values for variables 

Optional 
Labels for the variables 
Description
Histogram uncertain variables are typically used to model a set of
empirical data. The bin histogram (contrast: histogram_point_uncertain
) is a continuous aleatory
distribution characterized by bins of nonzero width where the
uncertain variable may lie, together with the relative frequencies of
each bin. Hence it can be used to specify a marginal probability
density function arising from data.
The histogram_bin_uncertain
keyword specifies the number of
variables to be characterized as continuous histograms. The required
subkeywords are: abscissas
(ranges of values the variable can take on) and either
ordinates
or
counts
(characterizing each
variable’s frequency information). When using histogram bin
variables, each variable must be defined by at least one bin (with two
bounding value pairs). When more than one histogram bin variable is
active, pairs_per_variable
can
be used to specify unequal apportionment of provided bin pairs among
the variables.
The abscissas
specification defines abscissa values (\(x\)
coordinates) for the probability density function of each histogram
variable. When paired with counts
, the specifications provide sets
of \((x,c)\) pairs for each histogram variable where \(c\) defines a
count (i.e., a frequency or relative probability) associated with a
bin. If using bins of unequal width and specification of probability
densities is more natural, then the counts
specification can be
replaced with an ordinates
specification (\(y\) coordinates) in order
to support interpretation of the input as \((x,y)\) pairs defining the
profile of a “skyline” probability density function.
Conversion between the two specifications is straightforward: a count/frequency is a cumulative probability quantity defined from the product of the ordinate density value and the \(x\) bin width. Thus, in the cases of bins of equal width, ordinate and count specifications are equivalent. In addition, ordinates and counts may be relative values; it is not necessary to scale them as all user inputs will be normalized.
To fully specify a binbased histogram with \(n\) bins (potentially of unequal width), \(n+1\) \((x,c)\) or \((x,y)\) pairs must be specified with the following features:
\(x\) is the parameter value for the left boundary of a histogram bin and \(c\) is the corresponding count for that bin. Alternatively, \(y\) defines the ordinate density value for this bin within a skyline probability density function. The right boundary of the bin is defined by the left boundary of the next pair.
the final pair specifies the right end of the last bin and must have a \(c\) or \(y\) value of zero.
the \(x\) values must be strictly increasing.
all \(c\) or \(y\) values must be positive, except for the last which must be zero.
a minimum of two pairs must be specified for each binbased histogram variable.
Examples
The pairs_per_variable
specification provides for the proper
association of multiple sets of \((x,c)\) or \((x,y)\) pairs with
individual histogram variables. For example, in this input snippet
histogram_bin_uncertain = 2
pairs_per_variable = 3 4
abscissas = 5 8 10 .1 .2 .3 .4
counts = 17 21 0 12 24 12 0
descriptors = 'hbu_1' 'hbu_2'
pairs_per_variable
associates the first 3 \((x,c)\) pairs from abscissas
and counts
{(5,17),(8,21),(10,0)} with one binbased histogram variable,
where one bin is defined between 5 and 8 with a count of 17 and another bin is defined between 8 and 10 with a count of 21. The following set of 4 \((x,c)\)
pairs {(.1,12),(.2,24),(.3,12),(.4,0)} defines a second binbased histogram variable containing three equalwidth bins with counts 12, 24, and 12 (middle
bin is twice as probable as the other two).
FAQ
Difference between bin and point histograms: A (continuous) bin histogram specifies bins of nonzero width, whereas a (discrete) point histogram specifies individual point values, which can be thought of as bins with zero width. In the terminology of LHS [WJ98], the bin pairs specification defines a “continuous linear” distribution and the point pairs specification defines a “discrete histogram” distribution (although the points are realvalued, the number of possible values is finite).