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performance of such services, we created a different benchmark application. This application uses the InfoBroker in the DINA node, representing a service logic application that needs access to local information. The load creating applications were also changed in a way that they generate load on the InfoBroker as well. As one can see in Figure 8.9, the time taken to complete the task increases as the number of load applications increases. Again at some point (around 50 load applications in our case) the system becomes less stable, but up to this point the execution time increases linearly with the number of load applications, and the InfoBroker seems to handle the load ef ciently. It is predicted that other brokers will perform similarly, depending of course on the speci c implementation of the broker. The actual performance of the CONTEXT system in real scenarios depends on many parameters and can be tested only when the system is deployed in large-scale con gurations (i.e., many representative applications executing in a realistic CONTEXT infrastructure). However, the preliminary results of the performance testing

>>> from ppf.date time import * >>> imm = nth imm of year >>> imm dates = [] >>> imm dates.append(imm(imm.first).get date(2005)) >>> imm dates.append(imm(imm.second).get date(2005)) >>> imm dates.append(imm(imm.third).get date(2005)) >>> imm dates.append(imm(imm.fourth).get date(2005)) >>> for t in imm dates: ... print t 2005-Mar-16 2005-Jun-15 2005-Sep-21 2005-Dec-21

The probabilistic learning methods were tested on ten pairs of databases with 1000 tuples each These databases were generated by ancestral sampling from Bayesian network for the Danish Jersey cattle blood type determination example (cf Figure 417 on page 130) The first database of each pair was used to induce a graphical model, the second to test this model The results were then averaged over all ten pairs As a baseline for comparisons we used the original graph the databases were generated from and a graph without any edges, that is, with independent attributes All networks are assessed by computing the log-likelihood of the data (natural logarithm; see, for example, Section 712) In order to avoid problems with impossible tuples, the probabilities were estimated with a Laplace correction39 of 1 The results are shown in Table 74.

In addition to the network quality this table shows the total number of edges (parents/conditions) and the number of (probability) parameters as a measure of the complexity of the network For the K2 algorithm [Cooper and Herskovits 1992], that is, the greedy selection of parent attributes wrt a topological order, the learned network is also compared to the original one by counting the number of additional and missing edges This is possible here, because any edge selected must have the same direction as in the original network Analogous numbers are not shown for the optimum weight spanning trees or the results of simulated annealing, because here the edges may have different directions and thus a comparison to the original network is not that straightforward.

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The results obtained for optimum weight spanning tree construction show that this simple method achieves a very good fit of the data, which is not surprising, since the original graph is rather sparse These results also show (Shannon) the bias of Shannon information gain Igain and the 2 measure towards many-valued attributes Although the number of edges is (necessarily) identical for all trees, for these two measures the number of parameters is consid39 The.

Unison contains both graphical and command-line tools for doing unison backups. It will try to run the graphical version by default. This may fail if you don t have a desktop running or if you re launching unison from within screen. To force unison to run in command line mode, add the -ui text option as follows:

which can be solved for a unique u by assigning uk > 0 for QI s11 fT Ug k argmaxdR s22 Se; s33 q uk 1 skk 1 2 then, depending on whether k 1, k 2, or k 3, k 1 r s11 1 u1 % 2 u2 % u3 % s12 2u1 s13 2u1 k 2 s12 2u2 r s22 1 2 s23 2u2 W k 3 b b b b s13 b b b b b 2u3 b b a HP C:133