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. 2020 Sep 29;22(10):1100. doi: 10.3390/e22101100
[D] [D]:={1,…,D}
〈·,·〉 inner product
MQ(u,v)=∥u−v∥Q Mahalanobis distance MQ(u,v)=∑i,j(ui−vi)(uj−vj)Qij, Q≻0
D(θ:θ′) parameter divergence
D[p(x):p′(x)] statistical divergence
D, D* Divergence and dual (reverse) divergence
Csiszár divergence If If(θ:θ′):=∑i=1Dθifθi′θi with f(1)=0
Bregman divergence BF BF(θ:θ′):=F(θ)−F(θ′)−(θ−θ′)⊤∇F(θ′)
Canonical divergence AF,F* AF,F*(θ:η′)=F(θ)+F*(η′)−θ⊤η′
Bhattacharyya distance Bα[p1:p2]=−log∫x∈Xp1α(x)p21−α(x)dμ(x)
Jensen/Burbea-Rao divergence JF(α)(θ1:θ2)=αF(θ1)+(1−α)F(θ2)−F(θ1+(1−α)θ2)
Chernoff information C[P1,P2]=−logminα∈(0,1)∫x∈Xp1α(x)p21−α(x)dμ(x)
F, F* Potential functions related by Legendre–Fenchel transformation
Dρ(p,q) Riemannian distance Dρ(p,q):=∫01∥γ′(t)∥γ(t)dt
B, B* basis, reciprocal basis
B={e1=∂1,…,eD=∂D} natural basis
{dxi}i covector basis (one-forms)
(v)B:=(vi) contravariant components of vector v
(v)B*:=(vi) covariant components of vector v
u⊥v vector u is perpendicular to vector v (〈u,v〉=0)
∥v∥=〈v,v〉 induced norm, length of a vector v
M, S Manifold, submanifold
Tp tangent plane at p
TM Tangent bundle TM=∪pTp={(p,v),p∈M,v∈Tp}
F(M) space of smooth functions on M
X(M)=Γ(TM) space of smooth vector fields on M
vf direction derivative of f with respect to vector v
X,Y,Z∈X(M) Vector fields
g=Σgijdxi⊗dxj metric tensor (field)
(U,x) local coordinates x in a chat U
∂i:=:∂∂xi natural basis vector
∂i:=:∂∂xi natural reciprocal basis vector
∇ affine connection
∇XY covariant derivative
∏c∇ parallel transport of vectors along a smooth curve c
∏c∇v Parallel transport of v∈Tc(0) along a smooth curve c
γ, γ∇ geodesic, geodesic with respect to connection ∇
Γij,l Christoffel symbols of the first kind (functions)
Γijk Christoffel symbols of the second kind (functions)
R Riemann–Christoffel curvature tensor
[X,Y] Lie bracket [X,Y](f)=X(Y(f))−Y(X(f)),∀f∈F(M)
∇-projection PS=argminQ∈SD(θ(P):θ(Q))
∇*-projection PS*=argminQ∈SD(θ(Q):θ(P))
C Amari–Chentsov totally symmetric cubic 3-covariant tensor
P={pθ(x)}θinΘ parametric family of probability distributions
E,M,ΔD exponential family, mixture family, probability simplex
PI(θ) Fisher information matrix of family P
PI(θ) Fisher Information Matrix (FIM) for a parametric family P
Pg Fisher information metric tensor field
exponential connection Pe∇ Pe∇:=Eθ(∂i∂jl)(∂kl)
mixture connection Pm∇ Pm∇:=Eθ(∂i∂jl+∂il∂jl)(∂kl)
expected skewness tensor Cijk Cijk:=Eθ∂il∂jl∂kl
expected α-connections PΓαijk:=−1+α2Cijk=Eθ∂i∂jl+1−α2∂il∂jl(∂kl)
≡ equivalence of geometric structures