### Table 2: General Probabilistic Population-Based Al- gorithm.

1999

Cited by 19

### Table 2. Maximum probabilistic reachability results for the IPv4 protocol

2003

"... In PAGE 12: ... The probabilistic reachability property we con- sider is the (minimum and maximum) probability of the host using an IP ad- dress which is already in use by another host. The results obtained in the case of maximum probabilistic reachability are given in Table2 . For results concerning minimum reachability probabilities see the PRISM web page [25].... In PAGE 12: ... The results obtained show the expected result: increasing the number of probes sent decreases the probability of the host using an IP address which is already in use (recall that the number specified by the standard is four). When the probability of message loss is 0, Table2 shows that the maximum probability is 0 for the the model reset (the model where the host clears its buffer) provided the host sends more than one probe. On the other hand, for the model no reset (when the host does not clear its buffer), even if the host sends more than one probe, this maximum reachability probability is greater than 0.... ..."

Cited by 19

### Table 2. Probabilistic non-repudiation protocol - Originator model

2002

Cited by 11

### Table 8: Behavior of the dynamic protocol in dynamic network environments

1996

"... In PAGE 24: ... cong. node #1 #1 16 1 #2 #1 16 6 #3 #2 6 1 #4 #2 6 3 #5 #3 1 5 #6 #3 1 10 Table 7: Dynamic network environments Table8 shows the behavior of the dynamic protocol in the dynamic network settings shown in Table 7. The table shows the time at which a split or a merge occurs.... ..."

Cited by 34

### Table 6.1: A comparison between the dynamic And-Or, the dynamic Paths and the dynamic probabilistic quorum system.

2005

### Table 2: Comparison of performance on practical examples;; the probabilistic

1998

"... In PAGE 8: ... We also tried a modi ed version of the EA which rst runs APGAN and then inserts the computed topological sort into the initial population. Table2 shows the results of applying GDPPO to the schedules generated by the various heuristics on several practical SDF graphs;; the satellite re- ceiver example is taken from [16], whereas the other examples are the same as considered in [3]. The probabilistic algorithms ran once on each graph and were aborted after 3000 tness evaluations.... In PAGE 8: ... Additionally, an exhaustive searchwithamaximum run-time of 1 hour was carried out;; as it only com- pleted in two cases 3 , the search spaces of these problems seem to be rather complex. In all of the practical benchmark examples in Table2 the results achieved by the EA equal or surpass those generated by RPMC. Compared to APGAN on these practical examples, the EA is neither inferior nor superior;; it shows both better and worse performance in two cases each.... ..."

Cited by 12

### Table 1 Examples of static probabilistic combination strategies.

2001

"... In PAGE 10: ..., a19a44a57 ). The static probabilistic combination strategies for the dependence informations in Figure 2 are shown in Table1 . The following proposition states that it is correct.... In PAGE 10: ... Recently, the probabilistic conjunction and disjunction strategies for ignorance, independence, positive correlation, negative correlation, and mutual exclu- sion have especially been discussed in [21] and [20]. To our knowledge, the strategies for the remaining dependence informations in Table1 have not been considered so far. 2.... In PAGE 12: ...resp., a90a75a52a93a60a216a90a22a57a188a109a56a90a25a52a25a60a71a68a70a69a137a90a22a57 ) for all probabilistic pairs a90a75a52 and a90a64a57 . For associative static or dynamic probabilistic disjunction strategies a60 and proba- bilistic pairs a90a75a52a44a14a95a105a95a105a95a105a44a14a77a90a32a72 with a73 a112a109 a83 , we write a74 a192 a165 a76a75 a52 a78a77 a72a37a79 a90 a192 to denote a90a75a52a93a60a199a68a95a68a95a68a53a60a216a90a80a72 . The following proposition identifies some static probabilistic conjunction and dis- junction strategies in Table1 that are commutative, associative, and distributive. Proposition 2.... In PAGE 37: ...10 (sketch). The statements can be easily verified along the static probabilistic combination strategies shown in Table1... In PAGE 38: ...13 (sketch). The statement can easily be verified along the static probabilistic combination strategies for ignorance, independence, positive correlation, and negative correlation shown in Table1 (see Proposition 2.9).... ..."

Cited by 15

### Table 6: Dynamic probabilistic inference: Estimated value of final state given first six observations. 500 repetitions.

2000

"... In PAGE 8: ... The problem was to infer the val- ue of the final state variable DCD8 given the observations DEBDBN DEBEBN BMBMBMBN DED8. Table6 again demonstrates that GIS has a sizeable advantage over standard importance sampling. (In fact, the greedy approach can be applied to particle filter- ing [IB96, KKR95] to obtain further improvements on this task, but space bounds preclude a detailed discussion.... ..."

Cited by 3

### Table 6: Dynamic probabilistic inference: Estimated value of final state given first six observations. 500 repetitions.

"... In PAGE 8: ... The problem was to infer the val- ue of the final state variable DC D8 given the observations DE BD BNDE BE BN BMBMBMBN DE D8 . Table6 again demonstrates that GIS has a sizeable advantage over standard importance sampling. (In fact, the greedy approach can be applied to particle filter- ing [IB96, KKR95] to obtain further improvements on this task, but space bounds preclude a detailed discussion.... ..."

### Table 2. The logical representation of the population dynamics library from Table 1.

"... In PAGE 4: ... Therefore, we designed domain-independent predicates that explicitly characterize the structure of processes and models. Table2 contains an encoding of the population dynamics library from Ta- ble 1. There are only two predicates in this representation: entity and process.... ..."