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195
How to improve Bayesian reasoning without instruction: Frequency formats
 Psychological Review
, 1995
"... Is the mind, by design, predisposed against performing Bayesian inference? Previous research on base rate neglect suggests that the mind lacks the appropriate cognitive algorithms. However, any claim against the existence of an algorithm, Bayesian or otherwise, is impossible to evaluate unless one s ..."
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Cited by 396 (29 self)
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Is the mind, by design, predisposed against performing Bayesian inference? Previous research on base rate neglect suggests that the mind lacks the appropriate cognitive algorithms. However, any claim against the existence of an algorithm, Bayesian or otherwise, is impossible to evaluate unless one
In sequential...
"... Today’s column deals with the theory of computability in a distributed system. It features a tutorial on this topic by Maurice Herlihy, Sergio Rajsbaum, and Michel Raynal. The tutorial focuses on a canonical asynchronous computation model, where processes communicate by writing to and reading from s ..."
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is used in order to simplify algorithms, and reduce the complexity of the solutions space one needs to explore for impossibility proofs. Second, concepts from combinatorial topology provide an understanding of the mathematical structure induced by possible executions of a protocol in this model. Many
Maximizing Impossibilities for Untestable Fault Identification
, 2002
"... This paper presents a new faultindependent method for maximizing local conicting value assignments for the purpose of untestable faults identification. The technique first computes a large number of logic implications across multiple timeframes and stores them in an implication graph. Then, by max ..."
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Cited by 6 (3 self)
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, by maximizing conflicting scenarios in the circuit, the algorithm identifies a large number of untestable faults that require such impossibilities. The proposed approach identifies impossible combinations locally around each Boolean gate in the circuit, and its complexity is thus linear in the number of nodes
Investment Aggregating Algorithm Defensive Forecasting Sequential Investment (1)
, 2010
"... • there are M stocks (0,1,...,M − 1) we can invest into — no cash or deposit (or one of the stocks is the deposit) — no inflation • time is discrete, t = 0,1,2,... • an investment decision is a vector γt = (γt,0, γt,1,..., γt,M−1) such that γt,i ∈ [0,1] and ∑M−1 j=0 γt,i = 1 — it shows the distribut ..."
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• there are M stocks (0,1,...,M − 1) we can invest into — no cash or deposit (or one of the stocks is the deposit) — no inflation • time is discrete, t = 0,1,2,... • an investment decision is a vector γt = (γt,0, γt,1,..., γt,M−1) such that γt,i ∈ [0,1] and ∑M−1 j=0 γt,i = 1 — it shows the distribution of our capital among the stocks — on step t − 1 we spend the fraction γt,j of our capital to buy stock j, j = 0,1,2,...,M − 1
HIERARCHICAL FORECASTING OF WEB SERVER WORKLOAD USING SEQUENTIAL
"... We propose a solution to the web server load prediction problem based on a hierarchical framework with multiple time scales. This framework leads to adaptive procedures that provide both longterm (in days) and shortterm (in minutes) predictions with simultaneous confidence bands which accommodate ..."
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not only serial correlation but also heavytailedness, and nonstationarity of the data. The longterm load is modeled as a dynamic harmonic regression (DHR), the coefficients of which evolve according to a random walk, and are tracked using sequential Monte Carlo (SMC) algorithms; whereas, the short
Ozone ensemble forecast with machine learning algorithms
 Journal of Geophysical Research
"... Abstract. We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations, the nume ..."
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Cited by 17 (6 self)
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Abstract. We apply machine learning algorithms to perform sequential aggregation of ozone forecasts. The latter rely on a multimodel ensemble built for ozone forecasting with the modeling system Polyphemus. The ensemble simulations are obtained by changes in the physical parameterizations
Ensemble Forecast of Analyses: Coupling Data Assimilation and Sequential Aggregation
, 2010
"... Abstract. Sequential aggregation is an ensemble forecasting approach that weights each ensemble member based on past observations and past forecasts. This approach has several limitations: the weights are computed only at the locations and for the variables that are observed, and the observational e ..."
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Cited by 10 (0 self)
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Abstract. Sequential aggregation is an ensemble forecasting approach that weights each ensemble member based on past observations and past forecasts. This approach has several limitations: the weights are computed only at the locations and for the variables that are observed, and the observational
Considering Unseen States as Impossible in Factored Reinforcement Learning
"... Abstract. The Factored Markov Decision Process (FMDP) framework is a standard representation for sequential decision problems under uncertainty where the state is represented as a collection of random variables. Factored Reinforcement Learning (FRL) is an Modelbased Reinforcement Learning approach ..."
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heuristics that considers as impossible the states that have not been seen so far. We derive an algorithm whose improvement in performance with respect to the standard approach is illustrated through benchmark experiments.
On the impossibility of amplifying the independence of random variables (Extended Abstract)
, 1994
"... ) Jinyi Cai Suresh Chari y Abstract In this paper we prove improved lower and upper bounds on the size of sample spaces which are required to be independent on specified neighborhoods. Our new constructions yield sample spaces whose size is smaller than previous constructions due to Schulman[ ..."
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independence. Finally, we enumerate all possible logical consequences of pairwise independent random bits. 1 Introduction Derandomization has proved to be an essential tool over the last few years in obtaining deterministic algorithms both in the sequential and parallel domain. For several problems the only
Data Assimilation in the Presence of Forecast Bias
, 1997
"... this article is to present a rigorous, yet practical, method for estimating forecast bias in an atmospheric data assimilation system. The method is fully consistent with the statespace approach of estimation theory, originally presented in the context of atmospheric data assimilation by Ghil et al. ..."
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Cited by 39 (2 self)
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, and this is the quantity we set out to estimate. We are then able to derive a rigorous sequential forecast bias estimation algorithm, whose implementation involves existing components of statistical data assimilation systems. Consequently one can incorporate forecast bias estimation in an operational system with only
Results 1  10
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195