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Probabilistic Progress Bars
Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user.
@inproceedings{kiefel14gcpr, title = {Probabilistic Progress Bars}, booktitle = {Conference on Pattern Recognition (GCPR)}, abstract = {Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user. }, volume = { 8753}, pages = {331--341}, series = {Lecture Notes in Computer Science}, editors = {Jiang, X., Hornegger, J., and Koch, R.}, publisher = {Springer}, month = sep, year = {2014}, slug = {kiefel14gcpr}, author = {Kiefel, Martin and Schuler, Christian and Hennig, Philipp}, month_numeric = {9} }