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Formal Bayesian Theory of Surprise Home Page. For a good summary of the story which does not require any maths.
Eric Mankin here. Formal Bayesian Theory of Surprise Home Page. The concept of surprise is central to sensory processing, adaptation.
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Pierre Baldi at the University of California. Irvine, we have developed a formal Bayesian definition of surprise.
Bayesian surprise quantifies how data affects natural or. The resulting theory of surprise is applicable across. Mathematical definition of surprise. We propose that surprise is a general, information- theoretic concept.
A. world that is purely deterministic and predictable in real- time for a. Second, surprise can only be. The same data may carry.
In probability and decision theory it can be shown that, under a small. Bayesian theory of probability. Therefore we. formally measure surprise elicited by quantifying the distance (or. This is. best done using the relative entropy or Kullback- Leibler (KL).
Thus, surprise is defined by the average. Note that KL is not symmetric but has well- known theoretical. The total number of wows experienced when.
What is the essence of surprise? To illustrate how surprise arises when data is observed, consider a. The observer has a number of co- existing. MTV. CNN, FOX, BBC, etc. Over the course of viewing the first few. CNN), the observer's. CNN in left. panel).
Consider next what happens if yet another video frame of the. Through Bayesian update, the new frame only minimally alters. In contrast, if a frame of snow was suddenly observed (middle. Through Bayesian update, this observation would. Indeed, no. more surprise arises after the observer's beliefs have stabilized. Thus surprise resolves the classical paradox.
Shannon. information. This paradox arises from the fact that there are many.
Thus. the entropy of snow is higher than that of natural scenes. Even when. the observer knows to expect snow, every individual frame of snow. Shannon information. Indeed, in a sample recording of 2. Shannon information per second.
MPEG4 to adaptively eliminate redundancy in both. TVSnow. TV: Snow Ratio. Shannon Information (Mbytes/s)0. Surprise (wows/s)5. Surprise attracts human attention. To test the surprise hypothesis - -- that Bayesian surprise attracts.
Each watched a subset (about. Clips. comprised outdoors daytime and nighttime scenes of crowded. Yet, We find that the surprise metric significantly. Shannon entropy). Outlook. The definition of surprise - -- as the distance between the posterior.
In addition, surprise- based behavioral measures, such as the. ADHD), as well as for quantitative comparison between humans. NIPS. 2. 00. 6More on the i. Lab publication server. Bayesian Theory of.
Surprise. Update Feb 2. Please see interesting related work by J. We. here briefly paraphrase the main argument of the authors. They. consider an agent in a non- deterministic Markov environment, who. Sk after executing an.
Si. To estimate pijk, the authors. Si and executed. action aj with state Sk as a result, to the total number of times. Si and executed aj (with any result). They. note that this ratio changes each time state Sk indeed results. Formally identical. Schimdhuber et al., propose to. KL divergence between p*ijk(t).
Reinforcement- driven information acquisition in. EC2 & CIE, Paris, 1. Reinforcement driven. Technical. Report, Fakultat fur Informatik, Technische Universitat Muenchen, 1. Note. how not all of the hits are relevant.