genome evolution: a sequence-centric approach lecture 6: belief propagation
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Genome evolution: a sequence-centric approach
Lecture 6: Belief propagation
Probabilistic models
Inference
Parameter estimation
Genome structure
Mutations
Population
Inferring Selection
(Probability, Calculus/Matrix theory, some graph theory, some statistics)
Simple Tree ModelsHMMs and variantsPhyloHMM,DBNContext-aware MMFactor Graphs
DPSamplingVariational apx.
EMGeneralized EM (optimize free energy)
Refs: HMM,simple tree: DurbinBasic BNs: HeckermanSampling: Mackey bookVariational: Jojic et al. paperLBP: Yedidia,Freeman, Weiss
Simple Tree: Inference as message passing
s
s
s s
s
s
sYou are P(H|our data)
You are P(H|our data)
I am P(H|all data)
DATA
Belief propagation in a factor graph
)(1
)|( aaa xZ
xP
• Remember, a factor graph is defined given a set of random variables (use indices i,j,k.) and a set of factors on groups of variables (use indices a,b..)
)( iia xm
• Think of messages as transmitting beliefs:
a->i : given my other inputs variables, and ignoring your message, you are x
i->a : given my other inputs factors and my potential, and ignoring your message, you are x
• xa refers to an assignment of values to the inputs of the factor a
• Z is the partition function (which is hard to compute)
• The BP algorithm is constructed by computing and updating messages:
• Messages from factors to variables:
• Messages from variables to factors: )( iai xm
(any value attainable by xi)->real values
Messages update rules:
)()(\)(
iicaiNc
iai xmxm
ia xx
jajiaNj
aaiia xmxxm )()()(\)(
a
iiaN \)(
a
i aiN \)(
• Messages from variables to factors:
• Messages from factors to variables:
The algorithm proceeds by updating messages:
• Define the beliefs as approximating single variables posterios (p(hi|s)):
)()()(
iiaiNa
ii xmxb
Algorithm:
Initialize all messages to uniformIterate until no message change:
Update factors to variables messagesUpdate variables to factors messages
• Why this is different than the mean field algorithm?
)()( ii hqhq
Beliefs on factor inputs
• This is far from mean field, since for example
)()(
)()()()(
\)()(
)()(
jjcajNcaNj
a
jjaNj
jajaNj
aaa
xmx
xbxmxxb
ia xxjjc
ajNcaNjaiiiid
iNdxmxxbxm )()()()(
\)()()(
ia xxjjc
ajNciaNjaiia xmxxm )()()(
\)(\)(
• The update rules can be viewed as derived from the:
1.requirement on the variables beliefs (bi)
2.requirement on the factor beliefs (ba)
3.Marginalization requirement:
Here’s how:
ia xx
aaii xbxb\
)()(
)()()(
iiaiNa
ii xmxb
BP on Tree = Up-Down
s4 s3
h2
h3e
s2 s1
h1
b a
c
d
)|Pr()|Pr()( 12111hshsxup ih
111)( 1 hbhach mmhm
)()()(
)()()(
2\
1
1\
1
11
11
smxhm
smxhm
bshx
bhb
ashx
aha
ib
ia
32
32
1
,313232 )|Pr()|Pr()()(
)(
hhhh
ih
hhhhhdownhup
xdown
3 2
2
3
33
1
31
)(),()(),(
)()(),(
)()()(
323313
3313
\31
h hehedc
hhehdc
hxchcchc
hmhhhhh
hmhmhh
hmxhmc
2 1
3
Loopy BP is not guaranteed to converge
X Y
Y
x
01
10
Y
x
01
10
1 1
0 0
This is not a hypothetical scenario – it frequently happens when there is too much symmetryFor example, most mutational effects are double stranded and so symmetric which can result
in loops.
The Bethe Free Energy
H. Bethe
• LBP was introduced in several domains (BNs, Coding), and is consider very practical in many cases.
• ..but unlike the variational approaches we studied before, it is not clear how it approximate the likelihood/partition function, even when it converges..
hh
hqhqshphqF )(log)()|,(log)( • Compare to the variational free energy:
Theorem: beliefs are LBP fixed points if and only if they are locally optimal for the Bethe free energy
iiii
aaabethe
aaabethe
BetheBetheBethe
xbxbdxbxbH
xxbU
HUF
)(log)()1()(log)(
)(log)(
• In the early 2000, Yedidia, Freeman and Weiss discovered a connection between the LBP algorithm and the Bethe free energy developed by Hans Bethe to approximate the free energy in crystal field theory back in the 40’s/50’s.
Generalization: Regions-based free energy
RR Aa
RXi
R caci 11
• Start with a factor graph (X,A)
• Introduce regions (XR,AR) and multipliers cR
• We require that:
• We will work with valid regions graphs:
)()()(
)(log)()(
)()()(
)(log)(
RRRRRR
xRRRRRR
xRRRRR
AaaRR
bHbUbF
xbxbbH
xExbbU
xxE
R
R
R
RR XaNAa )(
Region-based average energy
Region average energy
Region Entropy
Region Free energy
})({})({})({
)(})({
)(})({
R
R
R
RRRRR
RRRRR
RRRR
bHbUbF
bHcbH
bUcbU
Region-based entropy
Region-based free energy
Bethe regions are the factors neighbors set and single variables regions:
a
c
b
111 ccbac ccc
We compensate for the multiple counting of variables using the multiplicity constant
We can add larger regions
As long as we update the multipliers:
11 iia dcc
Ra
Rac
Rbc
RR
RR cc'
'1
Multipliers compensate on average, not on entropy
Claim: If the regions’ beliefs are exact then the average region-based energy is exact.
a x
aaaa
c
R x RaaaRRR
RRRRRR
a
RaR
R
xxbxxbcbUcbU )(log)()(log)()(})({)1(
We cannot guarantee much on the region-based entropy:
Claim: the region-based entropy is exact when the model is a uniform distributionProof: exercise. This means that the entropy count the correct number of degrees of
freedom – e.g. for binary variables, H=Nlog2
Definition: a region based free energy approximation is said to be max-ent normal if its region-based entropy is maximized when the beliefs are uniform.
An non max-ent approximation can minimize the region free energy by selecting erroneously high entropy beliefs!
Bethe’s region are max-ent normal
Claim: The Bethe regions gives a max-ent normal approximation (i.e. it maximize the region-based entropy on the uniform distribution)
a x aNi xiiiiaaaa
i xiiiiBethe
a ii
xbxbxbxbxbxbH)(
)(log)()(ln)()(log)(
Entropy Information
(maximal on uniform) (0 and minimal on uniform)
Start with a complete graph and binary factors
Add all variable triplets, pairs and singleton as regions
Generate multipliers:triplets = 1 (20 overall)pairs = -3 (15 overall)singletons = 6 (6 overall) ( guarantee consistency)
Example: A Non max-ent approximation
Look at the consistent beliefs:
The Region entropy (for any region) = ln2. The total region entropy is:
otherwise
xxxxxxb
otherwise
xxxxbxb kji
kjiji
jii 0
2/1),,(
0
2/1),(;5.0)0(
2ln112ln362ln452ln20 R
RRR HcH
We claimed before the entropy of the uniform distribution will be exact: 6ln2
RR
RR cc'
'1
We basically solve a variational problem:
While enforcing constraints on the regions’ beliefs:
Inference as minimization of region-based free energy
})({min RR bF
1)( Rx
RR xb
)()( ''\ '
RRxx
RR xbxbRR
Unlike the structured variational approximation we discussed before, and although the beliefs are (pairwise) compatible, we can have cases with locally optimal beliefs that are not representing a true global posterior distribution
C
BA
Y
x
4.01.0
1.04.0
1.04.0
4.01.0,
4.01.0
1.04.0CBA bbb
Y
x
1.04.0
4.01.0
Optimal region beliefs are identical to the factors:
5.0
5.0ib
Y
x
4.01.0
1.04.0
This is pairwise consistent, but cannot be the result of any joint distribution on the three vars
(we have a negative feedback loop here)
Claim: When it converges, LBP finds a minimum of the Bethe free energy.
Proof idea: we have an optimization problem (minimum energy) with constraints (beliefs are consistent and adds up to 1). We write down a Lagrangian that expresses both minimization goal and constraints, and show that it is minimized when the LBP update rules are holding.
Inference as minimization of region-based free energy
i iNa x xxaaiiiai
i xiii
a xaaaBethe
i ia
ia
xbxbx
xbxbFL
)( \
)]()()[(
1)(1)(
Important technical point: we shall assume that in the fixed point all beliefs are non zero. This can be shown to hold if all factors are “soft” (do not contain zero values for any assignment).
The Bethe Lagrangian
i iNa x xxaaiiiai
i xiii
a xaaaBethe
i ia
ia
xbxbx
xbxbFL
)( \
)]()()[(
1)(1)(
i x
iiiiia x
aaaaa x
aaaaBethe
iaa
xbxbdxbxbxxbF )(log)()1()(log)()(log)(
i iNa x xxaaiiiai
i xiii
a xaaa
i ia
i
a
xbxbx
xb
xb
)( \
)]()()[(
1)(
1)(
Large region beliefs are normalized
Variable region beliefs are normalized
Marginalization
The Bethe lagrangian
Take the derivatives with respect to each ba and bi:
))(1exp()()(
)(1)(log)(log)(
)(
)(
aNiiaiaaaaa
aNiiaiaaaaa
aia
xxxb
xxbxxb
L
i iNa x xxaaiiiai
i xiii
a xaaaBethe
i ia
ia
xbxbx
xbxbFL
)( \
)]()()[(
1)(1)(
i x
iiiiia x
aaaaa x
aaaaBethe
iaa
xbxbdxbxbxxbF )(log)()1()(log)()(log)(
)))((1
11exp()(
)()1)()(log1()(
)(
)(
aNiiaii
iii
iNaiaiiiii
ii
xd
xb
xxbdxb
L
Bethe minimum are LBP fixed points
))(exp()())(1exp()()()(
)(iai
aNia
aNiiaiaaaaa xxxxxb
)1
)(exp()))((
1
11exp()(
)()(
i
iai
iNaaNiiaii
iii d
xx
dxb
)(log)(log)(\)(
iicaiNc
iaiiai xmxmx
So here are the conditions:
And we can solve them if:
)()()(\)()(
iicaiNcaNi
aaa xmxxb
)()(1
1)(
)(\)()(iia
iNaiic
aiNci
iNaii xmxm
dxb
Giving us:
We saw before these conditions, with the marginalization constraint, are generating the update rules! So L minimum -> LBP fixed point is proven.
The other direction quite direct – see Exercise
LBP is in fact computing the lagrange multipliers – a very powerful observation
Generalizing LBP for region graphs
)()()()( '})(\{)P(')()P(
DDPRRDDPRDD
RRPRP
aaAa
RR xmxmxxbR
Parent-to-child beliefs:
A region graph is graph on subsets of nodes in the factor graph, with valid multipliers (as defined above)
RD(R) – Decedents of R
P(R)
RR Aa
RXi
R caci 11
• regions (XR,AR) and multipliers cR
• We require that:
• We will work with valid regions graphs:
RR XaNAa )(
RR
RR cc'
'1
P(D(R))\D(R)P(R) – Parents of R
D(R)
Generalizing LBP for region graphs
)()()()( '})(\{)P(')()P(
DDPRRDDPRDD
RRPRP
aaAa
RR xmxmxxbR
Parent-to-child algorithm:
RP
RP
x JJIRPDJI
JJIRPNJIaFaRRP xm
xmxxm
\
\
)(
)()()(
),(),(
),(),(
I
J
D(P)+P
Not D(P)+P
D(R) – Decedents of R
P(R) – Parents of R
P
RD(R)+R
IJ
D(P)+PP
RD(R)+R
N(I,J) = I not in D(P)+P J in D(P)+P but not D(R)+R
D(I,J) = I in D(P)+P but not D(R)+R J in D(R)+R
GLBP in practice
LBP is very attractive for users: really simple to implement, very fast
LBP performance is limited by the size of region assignments Xa which can grow rapidly with the factor’s degrees or the size of large regions
GLBP will be powerful when large regions can capture significant dependencies that are not captured by individual factors – think small positive loop or other symmetric effects
LBP messages can be computed synchronously (factors->variables->factors…), other scheduling options may boost up performance considerably
LBP is just one (quite indirect) way by which Bethe energies can be minimized. Other approaches are possible – which can be guaranteed to converge
The Bethe/Region energy minimization can be further constraint to force beliefs are realizable. This gives rise to the concept of Wainwright-Jordan marginal polytope and convex algorithms on it.