'Satisfying assignments' presentation slideshows

Umans Complexity Theory Lectures

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Synthesizing Complementary Circuits Automatically

Synthesizing Complementary Circuits Automatically. ShengYu Shen School of Computer National University of Defense Technology. Outline. Motivation Checking Complementary Condition Building Complementary Circuit Experimental results Summary. OSI seven-layer model. Complementary Circuits.

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Generating Hard Satisfiability Problems

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Bayesian network inference

Bayesian network inference. Given: Query variables: X Evidence ( observed ) variables: E = e Unobserved variables: Y Goal: calculate some useful information about the query variables Posterior P( X | e ) MAP estimate arg max x P( x | e )

416 views • 26 slides

Bayesian networks

Instructor: Vincent Conitzer. Bayesian networks. Rain and sprinklers example. sprinklers (Y). raining (X). P(X=1) = .3. P(Y=1) = .4. grass wet (Z). Each node has a conditional probability table (CPT). P(Z=1 | X=0, Y=0) = .1 P(Z=1 | X=0, Y=1) = .8 P(Z=1 | X=1, Y=0) = .7

258 views • 25 slides

CPS 570: Artificial Intelligence Bayesian networks

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CPS 570: Artificial Intelligence Bayesian networks

225 views • 22 slides

CPS 270: Artificial Intelligence cs.duke/courses/fall08/cps270/ Bayesian networks

Instructor: Vincent Conitzer. CPS 270: Artificial Intelligence http://www.cs.duke.edu/courses/fall08/cps270/ Bayesian networks. Specifying probability distributions. Specifying a probability for every atomic event is impractical

217 views • 21 slides