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Is Consumption Growth Consistent with Intertemporal Optimization? Evidence for the Consumer Expenditure Survey
 Journal of Political Economy
, 1995
"... In this paper we show that some of the predictions of models of consumer intertemporal optimization are in line with the patterns of nondurable expenditure observed in U.S. householdlevel data. We propose a flexible specification of preferences that allows multiple commodities and yields empirical ..."
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Cited by 314 (18 self)
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empirically tractable equations. We estimate preference parameters using the only U.S. micro data set with complete consumption information. We show that previous rejections can be explained by the simplifying assumptions made in previous studies. We also show that results obtained using good consump
Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Study.”
 Quarterly Journal of Economics
, 1997
"... ..."
What is a hidden Markov model?
, 2004
"... Often, problems in biological sequence analysis are just a matter of putting the right label on each residue. In gene identification, we want to label nucleotides as exons, introns, or intergenic sequence. In sequence alignment, we want to associate residues in a query sequence with homologous resi ..."
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Cited by 1344 (8 self)
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splice site consenses, codon bias, exon/intron length preferences, and open reading frame analysis all in one scoring system. How should all those parameters be set? How should different kinds of information be weighted? A second issue is being able to interpret results probabilistically. Finding a best
Policy gradient methods for reinforcement learning with function approximation.
 In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
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Cited by 439 (20 self)
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, if s was sampled from the distribution obtained by following π, then a ∂π(s,a) ∂θ Q π (s, a) would be an unbiased estimate of ∂ρ ∂θ . Of course, Q π (s, a) is also not normally known and must be estimated. One approach is to use the actual returns, corrects for the oversampling of actions preferred by π), which
Artificial Evolution for Computer Graphics
 Computer Graphics
, 1991
"... This paper describes how evolutionary techniques of variation and selection can be used to create complex simulated structures, textures, and motions for use in computer graphics and animation. Interactive selection, based on visual perception of procedurally generated results, allows the user to di ..."
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Cited by 316 (2 self)
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to direct simulated evolutions in preferred directions. Several examples using these methods have been implemented and are described. 3D plant structures are grown using fixed sets of genetic parameters. Images, solid textures, and animations are created using mutating symbolic lisp expressions. Genotjps
Information Retrieval as Statistical Translation
"... We propose a new probabilistic approach to information retrieval based upon the ideas and methods of statistical machine translation. The central ingredient in this approach is a statistical model of how a user might distill or "translate" a given document into a query. To assess the rele ..."
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Cited by 313 (6 self)
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the relevance of a document to a user's query, we estimate the probability that the query would have been generated as a translation of the document, and factor in the user's general preferences in the form of a prior distribution over documents. We propose a simple, well motivated model
BootstrapBased Improvements for Inference with Clustered Errors
, 2006
"... Microeconometrics researchers have increasingly realized the essential need to account for any withingroup dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate clusterrobust or sandwich standard errors that permit quite general ..."
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Cited by 303 (12 self)
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Microeconometrics researchers have increasingly realized the essential need to account for any withingroup dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate clusterrobust or sandwich standard errors that permit quite general
Probabilistic Matrix Factorization
"... Many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings. In this paper we present the Probabilistic Matrix Factorization (PMF) model which scales linearly with the number of observations and, more importantly, pe ..."
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Cited by 287 (5 self)
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, performs well on the large, sparse, and very imbalanced Netflix dataset. We further extend the PMF model to include an adaptive prior on the model parameters and show how the model capacity can be controlled automatically. Finally, we introduce a constrained version of the PMF model that is based
Parameterfree elicitation of utility and probability weighting functions.
 Manag. Sci.
, 2000
"... T his paper proposes a twostep method to successively elicit utility functions and decision weights under rankdependent expected utility theory and its ''more descriptive'' version: cumulative prospect theory. The novelty of the method is that it is parameterfree, and thus el ..."
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Cited by 183 (5 self)
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T his paper proposes a twostep method to successively elicit utility functions and decision weights under rankdependent expected utility theory and its ''more descriptive'' version: cumulative prospect theory. The novelty of the method is that it is parameterfree, and thus
StyleBased Inverse Kinematics
, 2004
"... This paper presents an inverse kinematics system based on a learned model of human poses. Given a set of constraints, our system can produce the most likely pose satisfying those constraints, in realtime. Training the model on different input data leads to different styles of IK. The model is repres ..."
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Cited by 211 (8 self)
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is represented as a probability distribution over the space of all possible poses. This means that our IK system can generate any pose, but prefers poses that are most similar to the space of poses in the training data. We represent the probability with a novel model called a Scaled Gaussian Process Latent
Results 1  10
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