
Projected Pólya Tree
One way of defining probability distributions for circular variables (di...
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Ignorance and the Expressiveness of Single and SetValued Probability Models of Belief
Over time, there have hen refinements in the way that probability distri...
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Calendarbased graphics for visualizing people's daily schedules
Calendars are broadly used in society to display temporal information, a...
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QuasiLinearTime Algorithm for Longest Common Circular Factor
We introduce the Longest Common Circular Factor (LCCF) problem in which,...
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Cycles in Causal Learning
In the causal learning setting, we wish to learn causeandeffect relati...
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Circularshift Linear Network Codes with Arbitrary Odd Block Lengths
Circularshift linear network coding (LNC) is a special type of vector L...
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Identifying latent classes with ordered categorical indicators
A Monte Carlo simulation was used to determine which assumptions for ord...
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Visualizing probability distributions across bivariate cyclic temporal granularities
Deconstructing a time index into time granularities can assist in exploration and automated analysis of large temporal data sets. This paper describes classes of time deconstructions using linear and cyclic time granularities. Linear granularities respect the linear progression of time such as hours, days, weeks and months. Cyclic granularities can be circular such as houroftheday, quasicircular such as dayofthemonth, and aperiodic such as public holidays. The hierarchical structure of granularities creates a nested ordering: houroftheday and secondoftheminute are singleorderup. Houroftheweek is multipleorderup, because it passes over dayoftheweek. Methods are provided for creating all possible granularities for a time index. A recommendation algorithm provides an indication whether a pair of granularities can be meaningfully examined together (a "harmony"), or when they cannot (a "clash"). Time granularities can be used to create data visualizations to explore for periodicities, associations and anomalies. The granularities form categorical variables (ordered or unordered) which induce groupings of the observations. Assuming a numeric response variable, the resulting graphics are then displays of distributions compared across combinations of categorical variables. The methods implemented in the open source R package `gravitas` are consistent with a tidy workflow, with probability distributions examined using the range of graphics available in `ggplot2`.
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