Random Walk stationary: ●convergence; stationary distribution; hitting time:time to reach a vertex; ● cover time:time to reach all vertices; mixing time:time to converge
Random Walk • stationary: • convergence; • stationary distribution; • hitting time: time to reach a vertex; • cover time: time to reach all vertices; • mixing time: time to converge
Mixing Time Markoy chain:t=(,P) ● mixing time:time to be close to the stationary distribution
Mixing Time Markov chain: M = (⌦, P) • mixing time: time to be close to the stationary distribution
Total Variation Distance two probability measures p,g over p,9∈[0,1]0 ∑p(x)=1∑q(c)=1 x∈2 x∈2 total variation distance between p and g: Ilp-allrv jllp-all >lp(z)-a(s) x∈2 equivalent definition: ID-grv=Rlp(A）-g(A 4/10 3/10 D 2/10 1/10 0 2 3 4
Total Variation Distance • two probability measures p, q over Ω: • total variation distance between p and q: • equivalent definition: p, q 2 [0, 1]⌦ X x2⌦ p(x)=1 X x2⌦ q(x)=1 kp qkT V = 1 2 kp qk1 = 1 2 X x2⌦ |p(x) q(x)| COUPLING OF MARKOV CHAINS 4/10 r-----. I I I I I I r----- I I I - + - -:- --- -{ -- I I I ____ -' L __________ I 3/10 2/10 1/10 o 2 3 4 x Figure 11.1: Example of variation distance. The areas shaded by upward diagonal lines correspond to values x where DI (x) D 2(x). The total area shaded by upward diagonal lines must equal the total area shaded by downward diagonal lines, and the variation distance equals one of these two areas. we run the chain for a finite number of steps. If we want to use this Markov chain to shuffle the deck, how many steps are necessary before we obtain a shuffle that is to uniformly distributed? To quantify what we mean by "close to uniform", we must introduce a distance measure. Definition 11.1: The variation distance between two distributions D, and D2 on (f countable state space S is given by 1 liD, - D211 = 2: LID,(x) - D2(x)l. XES A pictorial example of the variation distance is given in Figure 11.1. The factor 1/2 in the definition of variation distance guarantees that the variation distance is between 0 and l. It also allows the following useful alternative characterization. Lemma 11.1: Foran), A S, let Di(A) = LXEA Di(x)jori = 1,2. Then A careful examination of Figure 11.1 helps make the proof of this lemma transparent. Proof: Let S+ S be the set of states such that D,(x) D2(X), and let S- S the set of states such that D2 (x) > D, (x). Clearly, and But since D,(S) = D2(S) = 1, we have D,(S+) + D,(S-) = D2(S+) + D2(S-) = 1, 272 kp qkT V = max A✓⌦ |p(A) q(A)|
Mixing Time Markov chain:t=(,P) stationary distribution: p):distribution at time when initial state is △(t)=lp-π‖Tv △(t)=max△x(t) x∈2 Tx(e)=min{t|△x(t)≤e}T(e)=max T(e) x∈2 mixing time: Tmix =T(1/2e) rapid mixing: Tmix=(log2)0(1) △(k·Tmix)≤e-and(e)≤Tmix, In
Mixing Time Markov chain: M = (⌦, P) • mixing time: x(t) = kp(t) x ⇡kT V (t) = max x2⌦ x(t) ⌧x(✏) = min{t | x(t) ✏} ⌧ (✏) = max x2⌦ ⌧x(✏) ⌧mix = ⌧ (1/2e) stationary distribution: ⇡ p(t) x : distribution at time t when initial state is x rapid mixing: ⌧mix = (log |⌦|) O(1) (k · ⌧mix) ek ⌧ (✏) ⌧mix · ⇠ ln 1 ✏ ⇡ and
Coupling p,g:distributions over a distribution u over x is a coupling of p,q ifp(x)=>μ(，) q(c)=∑(y,x) y∈2 y∈2 9 0 2 p 2
Coupling p(x) = X y2⌦ µ(x, y) q(x) = X y2⌦ µ(y, x) a distribution µ over ⌦ ⇥ ⌦ is a coupling of p,q p,q : distributions over ⌦ if µ ⌦ ⌦ p q
Coupling Lemma Coupling Lemma 1.(X,Y)is a coupling of p,q>Pr[X≠Y]≥lp-qlrv 2.3 a coupling (X,Y)of p,g s.t.Pr[XY]=llp-allrv p() g(x) x
Coupling Lemma 1. (X,Y) is a coupling of p,q Pr[X 6= Y ] kp qkT V 2. ∃ a coupling (X,Y) of p,q s.t. Pr[X 6= Y ] = kp qkT V Coupling Lemma
Coupling of Markov Chains a coupling of =(P)is a Markov chain (X,Y) of state space x such that: o l both are faithful copies of the chain Pr[X++1=y Xt=x]=Pr[Yi+1=yYi=x]=P(x,y) once collides,always makes identical moves Xi=Y>Xi+1=Yi+1
• both are faithful copies of the chain • once collides, always makes identical moves Coupling of Markov Chains ⌦ Pr[Xt+1 = y | Xt = x] = Pr[Yt+1 = y | Yt = x] = P(x, y) Xt = Yt Xt+1 = Yt+1 is a Markov chain (Xt, Yt) of state space a coupling of M = (⌦, P) ⌦ ⇥ ⌦ such that:
Markov Chain Coupling Lemma Markov chain:=(,P) stationary distribution: p):distribution at time t when initial state isx △z(t)=lp-πlrv △(t)=max△z(t) x∈2 Markov Chain Coupling Lemma: (X:,Yi)is a coupling of=(2,P) △(t)≤nax Pr[Xt≠Yt|Xo=x,Yo=y x,y∈2
Markov Chain Coupling Lemma Markov chain: M = (⌦, P) x(t) = kp(t) x ⇡kT V (t) = max x2⌦ x(t) stationary distribution: ⇡ p(t) x : distribution at time t when initial state is x (Xt, Yt) is a coupling of M = (⌦, P) (t) max x,y2⌦ Pr[Xt 6= Yt | X0 = x, Y0 = y] Markov Chain Coupling Lemma:
distribution at timewhen initial state isx Markov Chain Coupling Lemma: (X,Y)is a coupling of =(2,P) △(t)≤max Pr[Xt≠Y|Xo=x,Yo=y x,y∈2 △()=竖p-xy ≤max x,y∈2 llsv ≤max Pr[Xt卡Yt|Xo=x,Yo=y c,y∈2 (coupling lemma)
(Xt, Yt) is a coupling of M = (⌦, P) (t) max x,y2⌦ Pr[Xt 6= Yt | X0 = x, Y0 = y] Markov Chain Coupling Lemma: p(t) x : distribution at time t when initial state is x (t) = max x2⌦ kp(t) x ⇡kT V max x,y2⌦ kp(t) x p(t) y kT V max x,y2⌦ Pr[Xt 6= Yt | X0 = x, Y0 = y] (coupling lemma)
=(P)stationary distribution: p:distribution at time when initial state isx △x(t)=lp-πlTv △(t)=max△z(t) x∈2 Tr(e)=min{t|△x(t)≤}T(e)=max Ta(e) x∈2 Markov Chain Coupling Lemma: (X:,Y:)is a coupling of=(,P) △(t)≤max Pr[Xt≠YEXo=x,Yo= x,y∈2 max Pr[Xt≠Y|Xo=x,Y%=列≤e>1 T(e)≤t c,y∈2
(Xt, Yt) is a coupling of M = (⌦, P) (t) max x,y2⌦ Pr[Xt 6= Yt | X0 = x, Y0 = y] Markov Chain Coupling Lemma: max x,y2⌦ Pr[Xt 6= Yt | X0 = x, Y0 = y] ✏ ⌧ (✏) t M = (⌦, P) x(t) = kp(t) x ⇡kT V (t) = max x2⌦ x(t) ⌧x(✏) = min{t | x(t) ✏} ⌧ (✏) = max x2⌦ ⌧x(✏) stationary distribution: ⇡ p(t) x : distribution at time t when initial state is x