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90
Relevance of Communicative acts
"... Why do we speak? Because we want to influence each other's behavior. The relevance of a speech act can measure its usefulness. In this paper I argue that (i) the relevance of a speech act depends on the `language game' one is involved in; (ii) notions of relevance can be defined using deci ..."
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Cited by 12 (1 self)
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Why do we speak? Because we want to influence each other's behavior. The relevance of a speech act can measure its usefulness. In this paper I argue that (i) the relevance of a speech act depends on the `language game' one is involved in; (ii) notions of relevance can be defined using decision, information and game theory, and can be used for linguistic applications; and (iii) the strategic considerations of participants in a conversation deserve our attention, especially when we consider mixedmotive games of imperfect information, for instance, to establish the common ground.
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 10 (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.
Applications of Lindley Information Measure to the Design of Clinical Experiments
 Aspects of Uncertainty
, 1994
"... this paper we consider applications of Lindley information measure to the design of clinical experiments. We review the decision theoretic foundations underlying the use of Lindley information, and discuss its role in constructing utility functions suitable for clinical applications. We derive and i ..."
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this paper we consider applications of Lindley information measure to the design of clinical experiments. We review the decision theoretic foundations underlying the use of Lindley information, and discuss its role in constructing utility functions suitable for clinical applications. We derive and interpret general firstorder conditions for the optimality of a design. We discuss examples: choosing the optimal fixed sample size of a clinical trial, and choosing the optimal followup time for patients in a survival analysis. We give special attention to the design of multicenter clinical trials. Research of D. A. Berry supported in part by the US Public Health Service under grant HS 0647501. Research of Giovanni Parmigiani and ISDS computing environment supported in part by NSF under grant DMS9305699. We are thankful to Chengchang Li, Peter Muller, Saurabh Mukhopadhyay and Dalene Stangl for helpful discussions. 1. INTRODUCTION From the point of view of decision making, information is anything that enables us to make a better decision, that is a decision with a higher expected utility. For example, an experiment that, irrespective of the outcome, will lead to the same decision that we would make prior to observing it, has no information content. Conversely, experiments able to lead to different decision are potentially of benefit. The expected change in utility can actually be used as a quantitative measure of the worth of an experiment in any given situation. This idea is about as old as Bayesian statistics (see Ramsey, 1990) and is discussed by Raiffa and Schlaifer (1961) and DeGroot (1984). The well known measure of information proposed by Lindley (1956) is the object of investigation in this paper. It can be seen as a very important special case of this general ap...
An Efficient Robust Concept Exploration Method and Sequential Exploratory Experimental Design
, 2004
"... ..."
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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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
"... ..."
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
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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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.
Spatial Statistics in Environmental Science
, 2000
"... this paper, sulfur dioxide measurements at 35 locations in the eastern U.S. over the time period 19891995 were characterized as functions of seasonal trends, meteorology, and an overall additive linear trend. A generalized additive model (Hastie and Tibshirani, 1990), applied to the logarithms of w ..."
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this paper, sulfur dioxide measurements at 35 locations in the eastern U.S. over the time period 19891995 were characterized as functions of seasonal trends, meteorology, and an overall additive linear trend. A generalized additive model (Hastie and Tibshirani, 1990), applied to the logarithms of weekly sulfur dioxide totals, was used to estimate the trend at each station, after adjusting for the seasonal and meteorological terms. In this example, instead of assuming the W = (w ij ) matrix in (39) is diagonal, w ij is estimated for each (i; j) pair by a jackknife procedure. The model (39) is then tted, again with a Gaussian semivariogram kernel (16). The resulting estimate of the trend surface is shown in Fig. 11. The importance of this analysis, in the context of evaluating improved regionalscale air quality resulting from electric utility emission reductions, is that it allows the characterization of estimated trends in sulfur dioxide, not only at the monitoring stations themselves, but also on a regional basis. The trends can be compared to corresponding changes in sulfur dioxide emissions to evaluate the impact of reduced emissions. For the period, 19891995, reduced emissions levels from large electric utilities are similar to the estimates of regional trends. 496 Smith 0.05 0 0 0.05 0.05 0.05 0.1 0.15 0.15 0.2 0.25 0.3 0.3 0.35 120 110 100 90 80 70 X
Probing public opinion: the state of Valencia experience
 In this volume
, 1996
"... This paper summarizes the procedures which have been set up during the last years at the Government of the State of Valencia, Spain, to systematically probe its public opinion as an important input into its decision processes. After a brief description of the electoral setup, we (i) outline the use ..."
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This paper summarizes the procedures which have been set up during the last years at the Government of the State of Valencia, Spain, to systematically probe its public opinion as an important input into its decision processes. After a brief description of the electoral setup, we (i) outline the use of a simulated annealing algorithm, designed to find a good design for sample surveys, which is based on the identification of representative electoral sections, (ii) describe the methods used to analyze the data obtained from sample surveys on politically relevant topics, (iii) outline the proceedings of election day —detailing the special problems posed by the analysis of exit poll, representative sections, and early returns data — and (iv) describe a solution to the problem of estimating the political transition matrices which identify the reallocation of the vote of each individual party between two political elections. Throughout the paper, special attention is given to the illustration of the methods with real data. The arguments fall entirely within the Bayesian framework. Keywords: