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<p class="MsoNormal" style="margin: 0in 0in 10pt"><font color="#000000"><font face="Calibri">Prof. David C Parkes, Computer Science, Bayesian Networks, Noisy-OR, Efficient CPTs, Continuous RVs in a Graphical Model, Full Bayes, Approximate Inference, Stochastic approximations, Deterministic approximations, Rejection sampling, Gibbs sampling, Harvard, Lecture Notes<p></p></font></font></p>
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qC = P(:F | C, :Fl, :M)=0.
qFl = P(:F | Fl, :C, :M)=0.
qM = P(:F | M, :Fl, :C)=0.
P(:F | C, M,:Fl) = qC£qM = 0.
P(:F | C, Fl,:M) = qC£qFl = 0.
P(:F | :C,:Fl,:M) = 1 …
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Boolean 1 param
Gaussian model N (H | ¹h, ¾^2 h) continuous 2 params
Probit model
Boolean (but w/ continuous parent) 2 parameters
Linear-Gaussian model S=1: N (C | at H + bt, ¾^2 t) S=0: N (C | af H + bf, ¾^2 f) continuous 6 parameters
cost
prob buy
¹b
Temporal attention model providing probability distribution over
user’s workload and task for selective filtering of messages
estimates user’s goal (e.g., workplace, home, friend) and trip segments taking
novelty detection (disables middle layer)
transportation mode speed location
observations: GPS
(Central Institute for Animal Disease Control, The Netherlands)