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2016


Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring

Mescheder, L., Nowozin, S., Geiger, A.

Arxiv, 2016 (article)

Abstract
We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sampler for weakly coupled discrete pairwise graphical models with strictly positive factors that requires almost no preprocessing and is easy to implement. Moreover, we show how our method can be combined with blocking to improve mixing. Even though our method leads to inferior mixing times compared to a sequential Gibbs sampler, we argue that our method is still very useful for large dynamic networks, where factors are added and removed on a continuous basis, as it is hard to maintain a graph coloring in this setup. Similarly, our method is useful for parallelizing Gibbs sampling in graphical models that do not allow for graph colorings with a small number of colors such as densely connected graphs.

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Map-Based Probabilistic Visual Self-Localization
Map-Based Probabilistic Visual Self-Localization

Brubaker, M. A., Geiger, A., Urtasun, R.

IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2016 (article)

Abstract
Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4m on average after 52 seconds of driving on maps which contain more than 2,150km of drivable roads.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]

2012


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Burn-in, bias, and the rationality of anchoring

Lieder, F., Griffiths, T. L., Goodman, N. D.

Advances in Neural Information Processing Systems 25, pages: 2699-2707, 2012 (article)

Abstract
Bayesian inference provides a unifying framework for addressing problems in machine learning, artificial intelligence, and robotics, as well as the problems facing the human mind. Unfortunately, exact Bayesian inference is intractable in all but the simplest models. Therefore minds and machines have to approximate Bayesian inference. Approximate inference algorithms can achieve a wide range of time-accuracy tradeoffs, but what is the optimal tradeoff? We investigate time-accuracy tradeoffs using the Metropolis-Hastings algorithm as a metaphor for the mind's inference algorithm(s). We find that reasonably accurate decisions are possible long before the Markov chain has converged to the posterior distribution, i.e. during the period known as burn-in. Therefore the strategy that is optimal subject to the mind's bounded processing speed and opportunity costs may perform so few iterations that the resulting samples are biased towards the initial value. The resulting cognitive process model provides a rational basis for the anchoring-and-adjustment heuristic. The model's quantitative predictions are tested against published data on anchoring in numerical estimation tasks. Our theoretical and empirical results suggest that the anchoring bias is consistent with approximate Bayesian inference.

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2012


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2006


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Die Effektivität von schriftlichen und graphischen Warnhinweisen auf Zigarettenschachteln

Petersen, L., Lieder, F.

Zeitschrift für Sozialpsychologie, 37(4):245-258, Verlag Hans Huber, 2006 (article)

Abstract
In der vorliegenden Studie wurde die Effektivität von furchterregenden Warnhinweisen bei jugendlichen Rauchern und Raucherinnen analysiert. 336 Raucher/-innen (Durchschnittsalter: 15 Jahre) wurden schriftliche oder graphische Warnhinweise auf Zigarettenpackungen präsentiert (Experimentalbedingungen; n = 96, n = 119), oder sie erhielten keine Warnhinweise (Kontrollbedingung; n = 94). Anschließend wurden die Modellfaktoren des revidierten Modells der Schutzmotivation (Arthur & Quester, 2004) erhoben. Die Ergebnisse stützen die Hypothese, dass die Faktoren «Schweregrad der Schädigung» und «Wahrscheinlichkeit der Schädigung» die Verhaltenswahrscheinlichkeit, weniger oder leichtere Zigaretten zu rauchen, vermittelt über den Mediator «Furcht» beeinflussen. Die Verhaltenswahrscheinlichkeit wurde dagegen nicht von den drei experimentellen Bedingungen beeinflusst. Auch konnten die Faktoren «Handlungswirksamkeitserwartungen» und «Selbstwirksamkeitserwartungen» nicht als Moderatoren des Zusammenhangs zwischen Furcht und Verhaltenswahrscheinlichkeit bestätigt werden.

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DOI [BibTex]

2006


DOI [BibTex]