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Probabilistic Progress Bars

2014

Conference Paper

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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.

Author(s): Kiefel, Martin and Schuler, Christian and Hennig, Philipp
Book Title: Conference on Pattern Recognition (GCPR)
Volume: 8753
Pages: 331--341
Year: 2014
Month: September

Series: Lecture Notes in Computer Science
Editors: Jiang, X., Hornegger, J., and Koch, R.
Publisher: Springer

Department(s): Empirical Inference, Perceiving Systems, Probabilistic Numerics
Research Project(s): Probabilistic Numerics
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.1007/978-3-319-11752-2_26
Event Name: GCPR 2014
Event Place: M{\"u}nster, Germany

Links: website+code
Attachments: pdf

BibTex

@inproceedings{kiefel14gcpr,
  title = {Probabilistic Progress Bars},
  author = {Kiefel, Martin and Schuler, Christian and Hennig, Philipp},
  booktitle = {Conference on Pattern Recognition (GCPR)},
  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},
  month_numeric = {9}
}