phycas lightning talk ievobio 2011
TRANSCRIPT
Estimating marginal likelihoods for phylogenetic modelsin Phycas
Phycas is a software package for Bayesian phylogenetic
inference (with support for ML searching planned).
Paul Lewis is the primary author. Mark Holder and Dave
Swofford are co-authors.
Written in C++ and Python (using boost-python to create
python bindings to C++ code).
Compiled versions and manual: http://www.phycas.org
Source: https://github.com/mtholder/Phycas
Bayesian model selection
• Use model averaging if we can “jump” between models, or
• Compare their marginal likelihood.
The Bayes Factor between two models:
B10 =Pr(D|M1)Pr(D|M0)
Pr(D|M1) =∫
Pr(D|θ,M1) Pr(θ)dθ
where θ is the set of parameters in the model.
Two simple estimators of the marginal likelihood
1. mean of likelihood evaluated at parameter values randomly
drawn from the prior.
2. harmonic mean of likelihood evaluated at parameter values
randomly drawn from the posterior (Newton and Raftery,
1994).
−2 −1 0 1 2
010
2030
40
Sharp posterior (black) and prior (red)
x
dens
ity
From Dr. Radford Neal’s blog
The Harmonic Mean of the Likelihood: Worst Monte
Carlo Method Ever
“The total unsuitability of the harmonic mean
estimator should have been apparent within an hour
of its discovery.”
Steppingstone sampling (Xie et al., 2010; Fan et al., 2010)
blends two distributions:
• the posterior, Pr(D|θ,M1) Pr(θ,M1)• a tractable reference distribution, π(θ)
pβ(θ|D,M1) =[Pr(D|θ,M1) Pr(θ,M1)]
β [π(θ)](1−β)
cβ
c0 = 1.0
Pr(D|M1) =c1c0
=(c1c0.38
)(c0.38c0.1
)(c0.1c0.01
)(c0.01c0
)=(c1
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)(����c0.1
�����c0.01
)(�����c0.01c0
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c1c0
=(
c1c0.38
)(c0.38c0.1
)(c0.1c0.01
)(c0.01c0
)
Photo by Johan Nobel http://www.flickr.com/photos/43147325@N08/4326713557/ downloaded from Wikimedia
Typically, Steppingstone sampling uses a series of slightly vaguer
distributions to estimate the ratio of normalizing constant:
−2 −1 0 1 2
010
2030
40
Steppingstone densities
x
dens
ity
A reference distribution over tree topologies
We must be able to:
1. calculate the probability for any tree topology,
2. center the distribution on the posterior,
3. control the “vagueness” of the distribution,
4. efficiently sample trees from the distribution.
Tree-Centered Independent-Split-Probability (TCISP)distribution
Argument: a tree with probabilities for each split.
Result: a probability distribution over all tree topologies.
A
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J L
HF
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KI
0.9
0.8 0.6 0.5
0.4 0.8
0.3
0.9Input: a focal treeto center the distributionwith split probabilities
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HF
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KI
We will keep the blue branchesand avoid the red ones
A G
D
EJ
LHF
CKI
A
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J LH
F
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KI
One of the many resolutionswhich avoid the red branches
A
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J L
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A
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J LH
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Counting trees:Bryant and Steel (2009) provide an O(n5) algorithm for
counting the number of trees that share no splits with another
tree.
Multitree steppingstone:
• Works on tiny trees (≤ 6 leaves) with no tuning;
• We are working on more efficient MCMC for larger trees;
• Code on: https://github.com/mtholder/Phycas/tree/sampling_ref_dist
Conclusions
• Do not trust the harmonic mean estimator of the marginal
likelihood.
• Take a look at Phycas: http://www.phycas.org (under
GPLv2.0; source on GitHub).
• Watch for multitree steppingstone is a more generic, usable
form soon.
• Tree-Centered Independent-Split-Probability (TCISP) distribution
may be useful in other contexts: likelihood-based supertrees,
or MCMC proposals.
Thanks: NSF AToL and iEvoBio
See: Xie et al. (2010); Fan et al. (2010); Lartillot
and Philippe (2006) for more discussion of estimating
marginal likelihoods.
References
Bryant, D. and Steel, M. (2009). Computing the distribution of a treemetric. IEEE IEEE/ACM Transactions on Computational Biology andBioinformatics, 6(3):420–426.
Fan, Y., Wu, R., Chen, M.-H., Kuo, L., and Lewis, P. O. (2010). Choosingamong partition models in bayesian phylogenetics. Molecular Biology andEvolution, page (advanced access).
Lartillot, N. and Philippe, H. (2006). Computing Bayes factors usingthermodynamic integration. Systematic Biology, 55(2):195–207.
Newton, M. A. and Raftery, A. E. (1994). Approximate bayesian inferencewith the weighted likelihood bootstrap. Journal of the Royal StatisticalSociety, Series B (Methodological), 56(1):3–48.
Xie, W., Lewis, P. O., Fan, Y., Kuo, L., and Chen, M.-H. (2010). Improving
marginal likelihood estimation for Bayesian phylogenetic model selection.Systematic Biology, 60(2):150–160.