1. Bayesian Networks Aldi Kraja Division of Statistical Genomics

2. Bayesian Networks and Decision Graphs. Chapter 1 Causal networks are a set of variables and a set of directed links between variables Variables represent events (propositions) A variable can have any number of states Purpose: Causal networks can be used to follow how a change of certainty in one variable may change certainty of other variables

3. Causal networks Fuel Fuel Meter Standing F, ½, E Start Y, N Y, N Clean Sparks Y, N Causal Network for a reduced start car problem

4. Causal Networks and d-separation Serial connection (blocking)Serial connection (blocking) ABC Evidence maybe transmitted through a serial connection unless the state of the variable in the connection is known. A and C and are d-separated given B When B is instantiated it blocks the communication between A and C

5. Causal networks and d-separation Diverging connections (Blocking)Diverging connections (Blocking) A BCE … Influence can pass between all children of A unless the state of A is known Evidence may be transmitted through a diverging connection unless it is instantiated.

6. Causal networks and d-separation Converging connections (opening)Converging connections (opening) A BCE … Case1: If nothing is known about A, except inference from knowledge of its parents => then parents are independent Evidence on one of the parents has no influence on other parents Case 2: If anything is known about the consequences, then information in one may tell us something about the other causes. (Explaining away effect) Evidence may only be transmitted through the converging connection If either A or one of its descendants has received evidence

7. Evidence Evidence on a variable is a statement of the certainties of its states If the variable is instantiated then the variable provides hard evidence Blocking in the case of serial and diverging connections requires hard evidence Opening in the case of converging connections holds for all kind of evidence

8. D-separation Two distinct variables A and B in a causal network are d-separated if, for all paths between A and B there is an intermediate variable V (distinct from A and B) such that: -The connection is SERIAL or DIVERGING and V is instantiated Or - the connection is CONVERGING and neither V nor any of V’s descendants have received evidence

9. Probability Theory The uncertainty raises from noise in the measurements and from the small sample size in the data. Use probability theory to quantify the uncertainty. P(B=r)=4/10 P(B=g)=6/10 ripe Wheat unripe Wheat Red fungus Gray fungus

10. Probability Theory The probability of an event is the fraction of times that event occurs out of the total number of trails, in the limit that the total number of trails goes to infinity

11. Probability Theory Sum rule: Product rule i=1……M j=1 …… L n ij Y=y i X=x i cici rjrj

12. Probability Theory i=1……M j=1 …… L n ij Y=y i X=x i cici rjrj

13. Probability Theory Symmetry property

14. Probability Theory P(W=u | F=R)=8/32=1/4 P(W=r | F=R)=24/32=3/4 P(W=u | F=G)=18/24=3/4 P(W=r | F=G)=6/24=1/4 P(F=R)=4/10=0.4 P(F=G)=6/10 =0.6 unripe Wheat Gray fungus Red fungus ripe Wheat 1 1

15. Probability Theory p(W=u)=p(W=u|F=R)p(F=R)+p(W=u|F=G)p(F=G) =1/4*4/10+3/4*6/10=11/20 p(W=r)=1-11/20=9/20 p(F=R|W=r)=(p(W=r|F=R)p(F=R)/p(W=r))= 3/4*4/10*20/9=2/3 P(F=G|W=u)=1-2/3=1/3 P(F=R)=4/10=0.4 P(F=G)=6/10 =0.6 unripped Wheat Gray fungus Red fungus ripe Wheat

16. Conditional probabilities Convergence connection (blocking) p(a|b)p(b)=p(a,b) p(a|b,c)p(b|c)=p(a,b|c) p(b|a)=p(a|b)p(b)/p(a) p(b|a,c)=p(a|b,c)p(b|c)/p(a|c) b ac p(a,b,c)=p(a|b)p(c|b)p(b) b ac p(a,b,c)/p(b)=p(a|b)p(c|b)p(b)/p(b) a╨c | b

17. Conditional probabilities Serial connection (blocking) p(a|b)p(b)=p(a,b) p(a|b,c)p(b|c)=p(a,b|c) p(b|a)=p(a|b)p(b)/p(a) p(b|a,c)=p(a|b,c)p(b|c)/p(a|c) bacbac p(a,b,c)=p(a)p(b|a)p(c|b) p(a,c|b)=p(a,b,c)/p(b)= p(a)p(b|a)p(c|b)/p(b)= p(a) {p(a|b)p(b)/p(a)} p(c|b)/p(b)=p(a|b)p(c|b) a╨c | b

18. Conditional probabilities Convergence connection (opening) p(a|b)p(b)=p(a,b) p(a|b,c)p(b|c)=p(a,b|c) p(b|a)=p(a|b)p(b)/p(a) p(b|a,c)=p(a|b,c)p(b|c)/p(a|c) b a c b ac p(a,b,c)=p(a)p(c)p(b|a,c) p(a,c|b)=p(a,b,c)/p(b)= p(a)p(c)p(b|a,c)/p(b) a╨c | 0 a╨c | b

19. Graphical Models We need probability theory to quantify the uncertainty. All the probabilistic inference can be expressed with the sum and the product rule. p(a,b,c)=p(c|a,b)p(a,b) p(a,b,c)=p(c|a,b)p(b|a)p(a) a c b DAG P(x 1,x 2,….,x K-1,x K )=p(x K |x 1,...,x K-1 )…p(x 2 |x 1 )p(x 1 )

20. Graphical Models DAG explaining joint distribution of x 1,…x 7 The joint distribution defined by a graph is given by the product, over all of the nodes of a graph, of a conditional distribution of each node conditioned on the variables corresponding to the parents of that node in the graph. x1x1 x2x2 x3x3 x4x4 x5x5 x6x6 x7x7