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Actionable Information in Vision
"... I propose a notion of visual information as the complexity not of the raw images, but of the images after the effects of nuisance factors such as viewpoint and illumination are discounted. It is rooted in ideas of J. J. Gibson, and stands in contrast to traditional information as entropy or coding l ..."
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Cited by 7 (6 self)
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I propose a notion of visual information as the complexity not of the raw images, but of the images after the effects of nuisance factors such as viewpoint and illumination are discounted. It is rooted in ideas of J. J. Gibson, and stands in contrast to traditional information as entropy or coding length of the data regardless of its use, and regardless of the nuisance factors affecting it. The noninvertibility of nuisances such as occlusion and quantization induces an “information gap ” that can only be bridged by controlling the data acquisition process. Measuring visual information entails early vision operations, tailored to the structure of the nuisances so as to be “lossless ” with respect to visual decision and control tasks (as opposed to data transmission and storage tasks implicit in traditional Information Theory). I illustrate these ideas on visual exploration, whereby a “Shannonian Explorer ” guided by the entropy of the data navigates unaware of the structure of the physical space surrounding it, while a “Gibsonian Explorer ” is guided by the topology of the environment, despite measuring only images of it, without performing 3D reconstruction. The operational definition of visual information suggests desirable properties that a visual representation should possess to best accomplish visionbased decision and control tasks. 1.
Bayesian Doptimal Designs for the Exponential Growth Model
 J. STATIST. PLAN. INF
, 1994
"... Bayesian optimal designs for nonlinear regression models are of some interest and importance in the statistical literature. Numerical methods for their construction are wellestablished, but very few analytical studies have been reported. In this paper, we consider an exponential growth model used e ..."
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Cited by 6 (1 self)
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Bayesian optimal designs for nonlinear regression models are of some interest and importance in the statistical literature. Numerical methods for their construction are wellestablished, but very few analytical studies have been reported. In this paper, we consider an exponential growth model used extensively in the modelling of simple organisms, and examine the explicit form of the Bayesian Doptimal designs. In particular, we show that D ` optimal designs for this model are balanced twopoint designs for all values of the parameters. We further derive explicit expressions for Bayesian Doptimal designs which are based on exactly two points of support, and provide necessary and sufficient conditions for such designs to exist. We illustrate our results by means of two examples.
Bayesian Input Variable Selection Using Posterior Probabilities and Expected Utilities
, 2002
"... We consider the input variable selection in complex Bayesian hierarchical models. Our goal is to find a model with the smallest number of input variables having statistically or practically at least the same expected utility as the full model with all the available inputs. A good estimate for the ..."
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Cited by 6 (1 self)
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We consider the input variable selection in complex Bayesian hierarchical models. Our goal is to find a model with the smallest number of input variables having statistically or practically at least the same expected utility as the full model with all the available inputs. A good estimate for the expected utility can be computed using crossvalidation predictive densities. In the case of input selection and a large number of input combinations, the computation of the crossvalidation predictive densities for each model easily becomes computationally prohibitive. We propose to use the posterior probabilities obtained via variable dimension MCMC methods to find out potentially useful input combinations, for which the final model choice and assessment is done using the expected utilities.
Comparing questions and answers: A bit of Logic, a bit of Language, and some bits of Information
 Sources and Streams of Information, ILLC
, 2001
"... ..."
Utility, informativity and protocols
 Proceedings of LOFT 5: Logic and the Foundations of the Theory of Games and Decisions
, 2001
"... this paper is to extend this investigation in several ways. The second contribution is to measure the relevance/utility of nonpartitional questions, and show how di#erent proposals (using either protocols or likelihood functions) come down to the same thing ..."
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Cited by 4 (3 self)
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this paper is to extend this investigation in several ways. The second contribution is to measure the relevance/utility of nonpartitional questions, and show how di#erent proposals (using either protocols or likelihood functions) come down to the same thing
SpatialTemporal Nonlinear Filtering Based on Hierarchical Statistical Models
 Investigacion Operativa : Test
, 2002
"... A hierarchical statistical model is made up generically of a data model, a process model, and occasionally a prior model for all the unknown parameters. The process model, known as the state equations in the filtering literature, is where most of the scientist's physical/chemical/biological knowl ..."
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Cited by 3 (1 self)
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A hierarchical statistical model is made up generically of a data model, a process model, and occasionally a prior model for all the unknown parameters. The process model, known as the state equations in the filtering literature, is where most of the scientist's physical/chemical/biological knowledge about the problem is used. In the case of a dynamically changing configuration of objects moving through a spatial domain of interest, that knowledge is summarized through equations of motion with random perturbations. In this paper, our interest is in dynamically filtering noisy observations on these objects, where the state equations are nonlinear. Two recent methods of filtering, the Unscented Particle filter (UPF) and the Unscented Kalman filter, are presented and compared to the better known Extended Kalman filter. Other sources of nonlinearity arise when we wish to estimate nonlinear functions of the objects positions; it is here where the UPF shows its superiority, since optimal estimates and associated variances are straightforward to obtain.
Identification of recurrent neural networks by Bayesian interrogation techniques
 J. of
, 2009
"... We introduce novel online Bayesian methods for the identification of a family of noisy recurrent neural networks (RNNs). We present Bayesian active learning techniques for stimulus selection given past experiences. In particular, we consider the unknown parameters as stochastic variables and use Ao ..."
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Cited by 3 (2 self)
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We introduce novel online Bayesian methods for the identification of a family of noisy recurrent neural networks (RNNs). We present Bayesian active learning techniques for stimulus selection given past experiences. In particular, we consider the unknown parameters as stochastic variables and use Aoptimality and Doptimality principles to choose optimal stimuli. We derive myopic cost functions in order to maximize the information gain concerning network parameters at each time step. We also derive the Aoptimal and Doptimal estimations of the additive noise that perturbs the dynamical system of the RNN. Here we investigate myopic as well as nonmyopic estimations, and study the problem of simultaneous estimation of both the system parameters and the noise. Employing conjugate priors our derivations remain approximationfree and give rise to simple update rules for the online learning of the parameters. The efficiency of our method is demonstrated for a number of selected cases, including the task of controlled independent component analysis.
Some Bayesian perspectives on statistical modelling
, 1988
"... I would like to thank my supervisor, Professor A. F. M. Smith, for all his advice and encourage ..."
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I would like to thank my supervisor, Professor A. F. M. Smith, for all his advice and encourage