Upcoming SlideShare
×

Like this presentation? Why not share!

# Bn

## on Mar 27, 2012

• 241 views

### Views

Total Views
241
Views on SlideShare
241
Embed Views
0

Likes
2
4
0

No embeds

### Report content

• Comment goes here.
Are you sure you want to
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n
• \n

## BnPresentation Transcript

• Learning the Structure of Dynamic Probabilistic Networks Matt Hink March 27, 2012
• Overview• Deﬁnitions and Introduction to DPNs• Learning from complete data• Experimental Results• Applications
• Regular probabilistic networks (Bayesian networks) are well established for representing probabilistic relationships among many random variables. Dynamic Probabilistic Networks (DPNs), however, extend this representation to the modeling ofstochastic evolution of a set of random variables over time. (Think “probabilistic state machines”)
• Notation• Capital letters (X,Y,Z)- sets of variables• X - Random variable of set X i• Val(X ) - Finite set of values of X i i• |X | - Size of Val(X ) i i• Lowercase italic (x,y,z)- set instantiations
• DPNs are an extension to the common Bayesian network representation where the probabilitydistribution changes with respect to time according to some stochastic process. Assume that X is a set of variables in a PN which vary according to time. Then Xi[t] is the value of the attribute Xi at time t, and X[t] is the collection of such variables.
• For simplicity’s sake, we assume that the stochastic process governing transitions is Markovian: P(X[t+1] | X[0...t]) = P(X[t+1] | X[t])That is, the probability of a certain instantiation isdependent only upon its immediate predecessor.
• We also assume the process is stationary, i.e., P(X[t+1] | X[t]) is independent of t.
• Given these two assumptions, we can describe a DPN representing the joint distribution over all possible trajectories of a process using two parts:A prior network B0 that speciﬁes a distribution over the initial states X[0]; and A transition network B-> over the variables X[0] ∪ X[1] which speciﬁes the transition probability P(X[t+1] | X[t]) for all t.
• A prior network (left) and transition network (right) for a dynamic probabilistic network
• In light of this structure, the joint distribution over the entire history of the DPN at time T is given as PB(x[0...T]) = PB0(x[0]) ∏(t=0...T-1) PB->(x[t+1] | x[t]) in other words, the product of all previous distributions.
• Learning fromComplete Data
• Common traditional methods:search algorithims using scoring methods (BIC, BDe) (given a dataset D) DPN methods: search algorithms using scoring methods! (given a dataset D, consisting of Nseq observations)
• So each entry in our dataset consists of an observation of a set of variables over time. The mth such sequence has length Nm and speciﬁes values for the variable set Xm[0...Nm] We then have Nseq instances of the initial state, andN = ∑m Nm instances of transitions. We can use these to learn the structure of the prior network and the transition network, respectively.
• BIC scores for DPNs• Let
• BIC scores for DPNs• We ﬁnd the log-likelihood using
• BIC scores for DPNs• And can then ﬁnd the BIC score using
• Experimental Results
• Application: Modeling driver behavior