5 resultados para Random Variable

em Deakin Research Online - Australia


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Probabilistic reasoning with belief (Bayesian) networks is based on conditional probability matrices. Thus it suffers from NP-hard implementations. In particular, the amount of probabilistic information necessary for the computations is often overwhelming. So, compressing the conditional probability table is one of the most important issues faced by the probabilistic reasoning community. Santos suggested an approach (called linear potential functions) for compressing the information from a combinatorial amount to roughly linear in the number of random variable assignments. However, much of the information in Bayesian networks, in which there are no linear potential functions, would be fitted by polynomial approximating functions rather than by reluctantly linear functions. For this reason, we construct a polynomial method to compress the conditional probability table in this paper. We evaluated the proposed technique, and our experimental results demonstrate that the approach is efficient and promising.

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Recently, a patchwork-based audio watermarking scheme has been proposed in [1], which embeds watermarks by modifying the means of absolute-valued discrete cosine transform (DCT) coefficients corresponding to suitable fragments. This audio watermarking scheme is more robust to common attacks than the existing counterparts. In this paper, we presents a detailed analysis of this audio watermarking scheme. We first derive a probability density function (pdf) of a random variable corresponding to the mean of an absolute-valued DCT fragment. Then, based on the obtained pdf, we show how watermarking parameters affect the performance of the concerned audio watermarking scheme. The analysis result provides a guideline for the selection of watermarking parameters. The effectiveness of our analysis is verified by simulations using a large number of real-world audio segments.

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Many methods to calculate message latencies for Controller Area Network (CAN) have previously been presented based upon the static worst-case behaviour of the system. With the use of modern simulation tools however, the behaviour of CAN networks can be simulated dynamically in order to find the likely worst-case response times for CAN messages. This paper shows the development of an automotive body control network model to be used as the basis for further simulations. A method to simulate the Worst-Case Response Time of this model is then presented, taking into account random queuing jitter.

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A new concept of counting time at fatigue processes is proposed, aimed to reach fractographic compatibility in cases of different loading sequences. Values of cycle effectivity are summarized to give the new reference time. The improvement is shown in application - textural fractography of three specimens loaded by constant cycle, constant cycle with periodic overloading, and a random block, respectively. In contrast to the conventional crack growth rate, the reference crack growth rate is related to common morphologic features of all fracture surfaces.

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Aposematic signal variation is a paradox: predators are better at learning and retaining the association between conspicuousness and unprofitability when signal variation is low. Movement patterns and variable colour patterns are linked in non-aposematic species: striped patterns generate illusions of altered speed and direction when moving linearly, affecting predators' tracking ability; blotched patterns benefit instead from unpredictable pauses and random movement. We tested whether the extensive colour-pattern variation in an aposematic frog is linked to movement, and found that individuals moving directionally and faster have more elongated patterns than individuals moving randomly and slowly. This may help explain the paradox of polymorphic aposematism: variable warning signals may reduce protection, but predator defence might still be effective if specific behaviours are tuned to specific signals. The interacting effects of behavioural and morphological traits may be a key to the evolution of warning signals. © 2014 The Author(s) Published by the Royal Society. All rights reserved.