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In determining which language and API to use for this exercise, there were two main options: The Perl API and Perl The XML-RPC API and a random scripting language Because we have devoted a very large portion of this book to Perl and the Perl API, I thought it best to choose the road less traveled. This choice was further strengthened by the fact that a kind soul named Keith Devens (www.keithdevens.com) has created and continues to maintain a fabulous XML-RPC library for PHP for the world to use. This library makes the formation, sending, and receiving of XML-RPC requests and responses as simple as a subroutine call. I wrote a companion XML-RPC library for Movable Type that specifically aids in the creation of the basic MT function calls (for example, publishing an entry, rebuilding a weblog, setting a category, and so on) in the form of PHP functions. Using these two libraries, communicating with MT via PHP has never been so easy, and this seemingly monumental and complex task is really quite simple and merely a matter of setup and configuration.

classi er, in other words, just the number of predictor variables times the number of distinct values of the target variable. Bayesian classi cation can be extended from categorical to continuous predictor variables, provided that we know the relevant probability distribution. Bayesian belief networks (BBNs) are designed to allow joint conditional independencies to be de ned among subsets of variables. BBNs, also called Bayesian networks or Bayes nets, take the form of a directed acyclic graph (DAG), where directed means that the arcs are traversed in one direction only, and acyclic means that no child node cycles back up to any progenitor. The nodes represent variables, and the arcs represent the (directed) dependence among the variables. In general, node A is a parent or immediate predecessor of node X , and node X is a descendant of node A, if there exists a directed arc from node A to node X . The intrinsic relationship among the variables in a Bayesian network is as follows: Each variable in a Bayesian network is conditionally independent of its nondescendants in the network, given its parents. The Bayesian network represents the joint probability distribution by providing that (1) a speci ed set of assumptions regarding the conditional independence of the variables, and (2) the probability tables for each variable, given its direct predecessors. For each variable, information regarding both (1) and (2) is provided.

slot assignment is done For example, although both have the same physical topology, the maxTSC is different in Fig 152a and 152b One intuitive solution to let each node know the maxTSC is to exchange local TSC information after the time slot assignment is done But this incurs additional overhead and latency We will discuss this later and show that a better solution is available Another problem revealed by the fact that the maxTSC is different in Fig 152a and 152b is that the maxTSC is not uniquely determined by the physical topology The order by which nodes choose time slots makes a difference to the time slot assignment, including the maxTSC From Eq (151), we know the minimum slots needed for a grid topology is 675.

Plural Nonidentifying Atomic The second avor-based transformation we will consider is the transformation needed for a Plural Nonidentifying Atomic (PNA) attribute type from the ERM For example, a PNA could represent a list of names, a list of numbers, or a list of dates Since this is a Nonidentifying AT, we need to transform it into something that can represent an unordered list of possibly duplicated atomic values from some domain In addition to the relation representing the containing ET, we also need to create a separate, new relation containing a Singular Nonidentifying Atomic (SNA) domain for each Plural Nonidentifying Atomic AT contained in the ET.