Page - 291 - in Differential Geometrical Theory of Statistics
Image of the Page - 291 -
Text of the Page - 291 -
Entropy2016,18, 442
â log(wâ˛jpâ˛j(x)) wâ˛jpâ˛j(x)
Figure1.Lowerenvelopeofparabolascorrespondingto theupperenvelopeofweightedcomponents
ofaGaussianmixturewithkâ˛=3components.
Toproceedoncetheenvelopeshavebeenbuilt,weneedtocalculatetwotypesofdefinite integrals
onthoseelementaryintervals: (i) theprobabilitymassinaninterval ⍠b
a p(x)dx=ÎŚ(b)âÎŚ(a)whereÎŚ
denotes the Cumulative Distribution Function (CDF); and (ii) the partial cross-entropy
â⍠ba p(x) logpâ˛(x)dx [26]. Thus letusdeďŹnethese twoquantities:
Ci,j(a,b) = â ⍠b
a wipi(x) log(wâ˛jp â˛
j(x))dx, (9)
Mi(a,b) = â ⍠b
a wipi(x)dx. (10)
ByEquations (7)and(8),weget theboundsofHĂ(m :mâ˛)as
LĂ(m :mâ˛)= â
r=1 k
â
s=1 Cs,δ(r)(ar,ar+1)â logkâ˛,
UĂ(m :mâ˛)= â
r=1 k
â
s=1 min {
Cs,δ(r)(ar,ar+1), Cs, (r)(ar,ar+1)âMs(ar,ar+1) logkⲠ}
. (11)
Thesizeof the lower/upperboundformuladependsontheenvelopecomplexity , the number
kofmixturecomponents,andtheclosed-formexpressionsof the integral termsCi,j(a,b)andMi(a,b).
Ingeneral,whenapairofweightedcomponentdensities intersect inatmost ppoints, theenvelope
complexity is related to the DavenportâSchinzel sequences [27]. It is quasi-linear for bounded
p=O(1), see [27].
Note that insymbolic computing, theRischsemi-algorithm[28] solves theproblemofcomputing
indeďŹnite integration in termsofelementary functionsprovidedthat thereexistsanoracle (hence the
termâsemi-algorithmâ) forcheckingwhetheranexpression isequivalent tozeroornot (however it is
unknownwhether thereexistsanalgorithmimplementingtheoracleornot).
Wepresentedthetechniquebyboundingthecross-entropy(andentropy) todeliver lower/upper
boundsontheKLdivergence.WhenonlytheKLdivergenceneedstobebounded,weratherconsider
theratioterm m(x)mâ˛(x). ThisrequirestopartitionthesupportX intoelementaryintervalsbyoverlayingthe
criticalpointsofboththelowerandupperenvelopesofm(x)andmâ˛(x),whichcanbedoneinlinear time.
Inagivenelementaryinterval, sincemax{kmini{wipi(x)},maxi{wipi(x)}}â¤m(x)⤠kmaxi{wipi(x)},
wethenconsider the inequalities:
max{kmini{wipi(x)},maxi{wipi(x)}}
kâ˛maxj{wâ˛jpâ˛j(x)} ⤠m(x)
mâ˛(x)⤠kmaxi{wipi(x)}
max{kâ˛minj{wâ˛jpâ˛j(x)},maxj{wâ˛jpâ˛j(x)}} . (12)
We now need to compute deďŹnite integrals of the form âŤb
a w1p(x;θ1)log w2p(x;θ2)
w3p(x;θ3) dx (see
AppendixBforexplicit formulaswhenconsideringscaledandtruncatedexponential families [17]).
(Thus forexponential families, theratioofdensities removes theauxiliarycarriermeasure term.)
291
Differential Geometrical Theory of Statistics
- Title
- Differential Geometrical Theory of Statistics
- Authors
- FrĂŠdĂŠric 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