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Entropy2016,18, 442
If1/2≤α<1, then θ¯=αθi+(1−α)θ′jbelongs to thenaturalparameterspaceMθ. ThereforeAαi,j(I)
isboundedandcanbecomputedfromtheCDFof p(x; θ¯)as
Aαi,j(I)= c α
i (c ′
j) 1−αexp(F (
θ¯ )−αF(θi)−(1−α)F(θ′j))∫
I p (
x; θ¯ )
dx. (41)
Theothercaseα> 1 ismoredifficult: if θ¯= αθi+(1−α)θ′j still lies inMθ, thenAαi,j(I) canbe
computedbyEquation(41).Otherwisewetry tosolve itbyanumerical integrator. This isnot idealas
the integralmaydiverge,orourapproximationmaybe too loose toconclude.Wepoint the reader
to [42] andEquations (61)–(69) in [35] for relatedanalysiswithmoredetails. As computingAαi,j(I)
only requiresO(1) time, theoverall computational complexity (without considering the envelope
computation) isO( ).
4.2.AdaptiveBounds
Thissectionderives theshape-dependentboundswhich improve thebasicbounds inSection4.1.
Wecanrewriteamixturemodelm(x) inaslab Ir as
m(x)=wζ(r)pζ(r)(x) ⎛⎝1+ ∑
i =ζ(r) wipi(x)
wζ(r)pζ(r)(x) ⎞⎠ , (42)
wherewζ(r)pζ(r)(x) isaweightedcomponent inm(x) servingasa reference.Weonlydiscuss thecase
that the reference is chosenas thedominatingcomponent, i.e., ζ(r)= δ(r). However it isworth to
note that theproposedboundsdonotdependonthisparticularchoice. Therefore theratio
wipi(x)
wζ(r)pζ(r)(x) = wi
wζ(r) exp ((
θi−θζ(r) )
t(x)−F(θi)+F(θζ(r)) )
(43)
canbebounded ina sub-rangeof [0,1]byanalyzing theextremevaluesof t(x) in the slab Ir. This
canbedonebecause t(x)usually consistsofpolynomial functionswithfinite criticalpointswhich
canbesolvedeasily.Correspondingly the function (
1+∑i =ζ(r) wipi(x)
wζ(r)pζ(r)(x) )
in Ir canbeboundedina
subrangeof [1,k],denotedas [ωζ(r)(Ir),Ωζ(r)(Ir)].Hence
ωζ(r)(Ir)wζ(r)pζ(r)(x)≤m(x)≤Ωζ(r)(Ir)wζ(r)pζ(r)(x). (44)
This formsbetterboundsofm(x) thanEquation (32)becauseeachcomponent in the slab Ir is
analyzedmore accurately. Therefore,we refine the fundamental boundsofm(x)by replacing the
Equations (34)and(35)with
cν(r)pν(r)(x) :=ωζ(r)(Ir)wζ(r)pζ(r)(x),
cδ(r)pδ(r)(x) :=Ωζ(r)(Ir)wζ(r)pζ(r)(x). (45)
(46)
Then, the improvedboundsofHα aregivenbyEquations (38)and(39)accordingto theabove
replaceddefinitionof cν(r)pν(r)(x)and cδ(r)pδ(r)(x).
To evaluate ωζ(r)(Ir) andΩζ(r)(Ir) requires iterating through all components in each slab.
Therefore thecomputationalcomplexity is increasedtoO( (k+k′)).
4.3.Variance-ReducedBounds
This section further improves the proposed bounds based on variance reduction [43].
Byassumption, α≥ 1/2, thenm(x)αm′(x)1−α ismore similar tom(x) rather thanm′(x). The ratio
299
Differential Geometrical Theory of Statistics
- Title
- Differential Geometrical Theory of Statistics
- Authors
- Frédéric Barbaresco
- Frank Nielsen
- Editor
- MDPI
- Location
- Basel
- Date
- 2017
- Language
- English
- License
- CC BY-NC-ND 4.0
- ISBN
- 978-3-03842-425-3
- Size
- 17.0 x 24.4 cm
- Pages
- 476
- Keywords
- Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
- Categories
- Naturwissenschaften Physik