Download Advances in Intelligent Data Analysis XIV: 14th by Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen PDF

By Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen

This ebook constitutes the refereed convention court cases of the 14th overseas convention on clever facts research, which was once held in October 2015 in Saint Étienne. France. The 29 revised complete papers have been conscientiously reviewed and chosen from sixty five submissions. the normal concentration of the IDA symposium sequence is on end-to-end clever aid for facts research. The symposium goals to supply a discussion board for uplifting study contributions that will be thought of initial in different major meetings and journals, yet that experience a very likely dramatic influence. To facilitate this, IDA 2015 will function tracks: a customary "Proceedings" music, in addition to a "Horizon" song for early-stage learn of probably ground-breaking nature.

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Extra info for Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne, France, October 22–24, 2015, Proceedings

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Table 4. (a) Execution times of example queries, and (b) Quality of results of sampling method as compared against the solutions of exact method. 6 Related Work Much attention in the Bayesian network literature has gone to the problem of finding explanations given some evidence. These explanation queries typically use a scoring function to find the best explanation. In contrast to queries like MAP and MPE, we do not fix which variables must be in or not in the pattern, instead we conceptually search over all possible marginalizations.

Our queries below will enumerate all satisfying BN patterns. Example Constraint-Based Queries. Assume the manager of a New York car insurance company has just obtained a Bayesian network that describes the factors influencing cost claims of customers, cf. the in Fig. 1. She wants to analyze the network to be able to assess costs, get more insight and provide recommendations to her personnel. In order to do so, she is interested in exploring patterns of interest in the network and poses a number of queries.

2. Arithmetic circuit for a BN with 3 vari2)} we set λ2,1 = 1, λ2,2 = ables with domain {1, 2} with X1 the parent of 0, λ3,1 = 0, λ3,2 = 1. X1 is not in X2 and X3 . Square boxes represent CP variables. the pattern and needs to be marginalized away, so we set ∀k ∈ D(X1 ) : λ1,k = 1. Then, one computes the values of the internal AC nodes bottom-up, according to their operation (× or +). The value of the root node is the requested probability. This can be encoded in CP for arbitrary ACs: for each indicator variable λi,j in the AC, we introduce a Boolean CP variable Bi,j ; the relation between the indicator variables and the CP variables Qi is then modeled by the following constraints (recall that Qi = 0 means variable Xi is not in the pattern): 1 1,1 2,1 3,2 1,1 3,2 Qi = 0 → ∧j (Bi,j = 1) Qi = k → (Bi,k = 1) ∧ (∧j=k (Bi,j = 0)) 1,2 2,2 1,2 2,2 3,1 2,1 3,1 ∀i ∀i, ∀k = 0 We then introduce real-valued variable P , which will represent the computed probability.

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