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Morphing the Hugin and Shenoy–Shafer Architectures
"... Abstract. The Hugin and Shenoy–Shafer architectures are two variations on the jointree algorithm, which exhibit different tradeoffs with respect to efficiency and query answering power. The Hugin architecture is more time–efficient on arbitrary jointrees, avoiding some redundant computations perfor ..."
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Abstract. The Hugin and Shenoy–Shafer architectures are two variations on the jointree algorithm, which exhibit different tradeoffs with respect to efficiency and query answering power. The Hugin architecture is more time–efficient on arbitrary jointrees, avoiding some redundant computations
PARIS–SAN DIEGO–SAN FRANCISCO–SINGAPORE–SYDNEY–TOKYOHandbook of Knowledge Representation Edited by
"... No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher ..."
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No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher
Informatica Ingenieur geboren te Den Helder. Dit proefschrift is goedgekeurd door de promotoren:
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Bayesian Reasoning and Machine Learning
"... V a calligraphic symbol typically denotes a set of random variables........ 7 dom(x) Domain of a variable.................................................... 7 x = x The variable x is in the state x.......................................... 7 p(x = tr) probability of event/variable x being in the st ..."
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V a calligraphic symbol typically denotes a set of random variables........ 7 dom(x) Domain of a variable.................................................... 7 x = x The variable x is in the state x.......................................... 7 p(x = tr) probability of event/variable x being in the state true................... 7 p(x = fa) probability of event/variable x being in the state false................... 7 p(x, y) probability of x and y................................................... 8 p(x ∩ y) probability of x and y................................................... 8 p(x ∪ y) probability of x or y.................................................... 8 p(xy) The probability of x conditioned on y................................... 8 X ⊥YZ Variables X are independent of variables Y conditioned on variables Z. 11 X ⊤YZ Variables X are dependent on variables Y conditioned on variables Z.. 11 x f(x) For continuous variables this is shorthand for ∫ f(x)dx and for discrete variables means summation over the states of x, ∑ x