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Differential Geometrical Theory of Statistics
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Entropy2016,18, 421 When there is no risk of confusion, we may abbreviate M(t;N) to M and fN,i(t;r) to fN(r), or even to f(r)—so that (A1) becomes M = f(0). Again, we may write ∂rM(t;N)/∂tri as Mr, ∂r+sM(t;N)/∂tri∂t s j asMr,s and∂ r+s+uM(t;N)/∂tri∂t s j∂t u l asMr,s,u,withsimilarconventions forhigher ordermixedderivatives. We can nowuse this to explicitly calculate lowordermoments of the count vectors. Using E(nri)= ∂ rM(t;N)/∂tri|t=0, theïŹrstNmomentsofninowfollowfrom(A1)andrepeateduseof (A2), notingthatm(0)=1and ai(0)=πi. Inparticular, theïŹrst6momentsofeachni canbeobtainedas follows,whereN≄6 isassumed. Using(A1)and(A2),wehave M1= f(1) M2= f(2)+ f(1) M3= f(3)+2f(2)+ f(2)+ f(1)= f(3)+3f(2)+ f(1) M4= f(4)+ 6f(3)+ 7f(2)+ f(1) M5= f(5)+10f(4)+25f(3)+15f(2)+ f(1) M6= f(6)+15f(5)+65f(4)+90f(3)+31f(2)+ f(1). Substituting in,wehave E(ni)= Nπi E(n2i)=N(2)π 2 i + Nπi E(n3i)=N(3)π 3 i + 3N(2)π 2 i + Nπi E(n4i)=N(4)π 4 i + 6N(3)π 3 i + 7N(2)π 2 i + Nπi E(n5i)=N(5)π 5 i +10N(4)π 4 i +25N(3)π 3 i +15N(2)π 2 i + Nπi E(n6i)=N(6)π 6 i +15N(5)π 5 i +65N(4)π 4 i +90N(3)π 3 i +31N(2)π 2 i +Nπi. Thiscanbeformalised in the followingLemma LemmaA1. The integer coefïŹcients inanyexpansion Mr= r ∑ s=1 cr(s)f(s) (1≀ r≀N) canbecomputedusingcr(1)= cr(r)=1 together, for r≄3,with theupdate: cr(s)= cr−1(s−1)+scr−1(s) (1< s< r). Wenote that ifMr is requiredfor r>N,wemayrepeatedlydifferentiate MN= N ∑ s=1 cN(s)f(s) w.r.t. ti,notingthat f(N)=N!aNi nolongerdependsonm(t)sothat, forallh>0,∂ hf(N)/∂thi =N hf(N). Mixedmomentsofanyordercanbederivedfromthoseof lowerorder,exploitingthefact that ai dependson tonlyvia ti. We illustrate this byderiving those required for the secondand third momentsofW. First consider themixedmoments requiredfor thesecondmomentofW. Ofcourse,Var(W)=0 ifk=0.Otherwise,k>0,andcomputingVar(W) requiresE(n2in 2 j) for i = j.WeïŹndthisas follows, assumingN≄4. 338
zurĂŒck zum  Buch Differential Geometrical Theory of Statistics"
Differential Geometrical Theory of Statistics
Titel
Differential Geometrical Theory of Statistics
Autoren
Frédéric Barbaresco
Frank Nielsen
Herausgeber
MDPI
Ort
Basel
Datum
2017
Sprache
englisch
Lizenz
CC BY-NC-ND 4.0
ISBN
978-3-03842-425-3
Abmessungen
17.0 x 24.4 cm
Seiten
476
Schlagwörter
Entropy, Coding Theory, Maximum entropy, Information geometry, Computational Information Geometry, Hessian Geometry, Divergence Geometry, Information topology, Cohomology, Shape Space, Statistical physics, Thermodynamics
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Differential Geometrical Theory of Statistics