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Bayesian Networks
book

Bayesian Networks

by Marco Scutari, Jean-Baptiste Denis
June 2014
Intermediate to advanced content levelIntermediate to advanced
241 pages
6h 20m
English
CRC Press
Content preview from Bayesian Networks
More Complex Cases: Hybrid Bayesian Networks 75
PR Mu (1, p = (0.7, 0.2, 0.1))
CL Beta (3, 1)
G1 | PR = p, CL = c P ois (c × g (p)))
G2 | G1 = g
1
Pois (10g
1
)
TR | G1 = g
1
Ber
logit
1
g
1
5
2.5

LO | G2 = g
2
, TR = t ncχ
2
1,
g
2
×
1
2t
3

2
!
Table 3.2
Probability distributions proposed for the DAG shown in Figure 3.4. g is
a known function giving the potential of G1 for a given class of the last
crop: g(1) = 1, g(2) = 3, g(3) = 10. Ber denotes a Bernoulli distribution, P ois
denotes a Poisson distribution and ncχ
2
denotes a non-central Chi-square
distribution (see Appendix B for their definitions and fundamental prope rties).
parameters of the distributions ...
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Publisher Resources

ISBN: 9781482225587