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Our calculations have determined that according to the Bayes Net model built from the training set, instance 3 is classi ed as positive with probability 0.577. Again, our hand-calculated probabilities agree with those produced by the WEKA model, as shown in Table 5.9. Clearly, our results indicate that hand-computing the probability values for a network of moderate size is a nontrivial task.
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where the last equality follows from the fact that the maximum is achieved by putting all the weight on the index i maximizing D(pi q) in (13.28). It also follows that q = q . This completes the proof. Thus, the channel capacity of the channel from to X is the minimax expected redundancy in source coding. Example 13.1.1 Consider the case when X = {1, 2, 3} and takes only two values, 1 and 2, and the corresponding distributions are p1 = (1 , , 0) and p2 = (0, , 1 ). We would like to encode a sequence of symbols from X without knowing whether the distribution is p1 or p2 . The arguments above indicate that the worst-case optimal code uses the
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extrinsic information transfer function mutual information function number of encoders in a multiple turbo code inverse of the mutual information function metric of a given event x log likelihood ratio for bit bi conditioned log likelihood ratio given the received sequence Y, for bit bi measure of the channel signal-to-noise ratio channel information for a turbo decoder, jth iteration extrinsic log likelihood ratio for bit bi extrinsic log likelihood ratio for bit bi , j-th iteration conditioned log likelihood ratio given the received sequence Y, for bit bi , jth iteration size of a block interleaver 2 random variable with zero mean value and variance Y probability distribution of a discrete random variable source marginal distribution function probability density function of a priori estimates A for X = x probability density function of extrinsic estimates E for X=x the angular coef cient of a linear interleaver channel transition probability linear shift of a linear interleaver generic vector or sequence of states of a Hidden Markov source current state value previous state value vector or sequence of n random variables generic vector or sequence of random variables
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After completing the planning stage of your project, you are ready to do some serious design work. But wait you won t want to jump straight into Flash until you ve got everything in order. First and foremost, you need access to everything you need to complete the project. In other words, you need to have everything at the ready before creating your production. There s nothing more jarring than being in the middle of a project where your creative juices are flowing only to discover you re missing an important piece needed to complete the puzzle.
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where PL is the RF signal propagation path loss based on distance d between the AP and the MS, f is the carrier frequency in megahertz, and n is the path loss exponent. In a similar fashion to (2.8), Ico_ij is de ned based on the cochannel interference within domain j only:
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