Table Of ContentANALYSIS OF CONVERGENCE RATES OF SOME GIBBS
SAMPLERS ON CONTINUOUS STATE SPACES
AARON SMITH
1. Abstract
2
1
0 We use a non-Markovian coupling and small modifications of techniques from the
2 theory of finite Markov chains to analyze some Markov chains on continuous state
n spaces. The first is a generalization of a sampler introduced by Randall and Winkler,
u
the second a Gibbs sampler on narrow contingency tables.
J
2
1 2. Introduction
] The problem of sampling from a given distribution on high-dimensional continuous
R
spaces arises in the computational sciences and Bayesian statistics, and a frequently-
P
. used solution is Markov chain Monte Carlo (MCMC); see [16] for many examples.
h
t Because MCMC methods produce good samples only after a lengthy mixing period,
a
a long-standing mathematical question is to analyze the mixing times of the MCMC
m
algorithms which are in common use. Although there are many mixing conditions,
[
the most commonly used is called the mixing time, and is based on the total variation
2
distance:
v
5 For measures ν, µ with common measurable σ-algebra A, the total variation dis-
1
tance between µ and ν is
4
5
||µ−ν|| = sup(µ(A)−ν(A))
. TV
8 A∈A
0
1 For an ergodic discrete-time Markov chain Xt with unique stationary distribution
1 π, the mixing time is
:
v
τ((cid:15)) = inf{t : ||L(X )−π|| < (cid:15)}
i t TV
X
r AlthoughmostscientificandstatisticalusesofMCMCmethodsoccurincontinuous
a
state spaces, much of the mathematical mixing analysis has been in the discrete
setting. The methods that have been developed for discrete chains often break down
when used to analyze continuous chains, though there are some efforts, such as [28]
[24] [18], to create general techniques. This paper extends the author’s previous work
in [26] and work of Randall and Winkler [22], and attempts to provide some more
Date: June 13, 2012.
1
2 AARON SMITH
examples of relatively sharp analyses of continuous chains similar to those used to
develop the discrete theory.
The first process that we analyze is a Gibbs sampler on the simplex with a very
restricted set of allowed moves. Fix a finite group G of size n with symmetric gen-
erating set R of size m. For unity of notation, label the group elements with the
integers from 1 to n. We consider the process X [g] on the simplex ∆ = {X ∈
t G
Rn|(cid:80) X[g] = 1;X[g] ≥ 0}. At each step, choose g ∈ G, r ∈ R and λ ∈ [0,1]
g∈G
uniformly, and set
X [g] = λ(X [g]+X [gr])
t+1 t t
(1) X [gr] = (1−λ)(X [g]+X [gr])
t+1 t t
For all other h ∈ G set X [h] = X [h]. Let U be the uniform distribution on
t+1 t G
∆ ; this is also the stationary distribution of X . Also consider a random walk Z
G t t
on G, where in each stage we choose g ∈ G and r ∈ R uniformly at random and
set Z = gr if Z = g, set Z = g if Z = gr, and Z = Z otherwise. This is
t+1 t t+1 t t+1 t
the standard simple random walk on the Cayley graph, slowed down by a factor of
about n. Let γ be the spectral gap of the walk Z , and follow the notation that L(X)
(cid:98) t
denotes the distribution of a random variable X.
Theorem1(ConvergenceRateforGibbsSamplerwithGeometry). ForT > 56k+178 log(n),
γ
(cid:98)
||L(X )−U || ≤ 14n−k
T G TV
and conversely for T < k,
γ
(cid:98)
1
||L(X )−U || ≥ e−k −3n−1
T G TV 3
2
Thissubstantiallygeneralizes[22]and[26],fromsamplerscorrespondingtoG = Z ,
n
and R = {1,−1} or R = Z \{0} respectively, to general Cayley graphs. In addition
n
to being of mathematical interest, this process is an example of a gossip process
with some geometry, studied by electrical engineers and sociologists interested in how
information propagates through networks; see [25] for a survey.
The proof of the upper bound will use an auxilliary chain similar to that found
in [22], a coupling argument improved from [26], and an unusual use of comparison
theory from [9]. The proof of the lower bound is elementary.
The next example consists of narrow contingency tables. Beginning with the work
of Diaconis and Efron [6] on independence tests, there has been interest in finding
efficient ways to sample uniformly from the collection of integer-valued matrices with
given row and column sums. A great deal of this effort has been based on Markov
chainMonteCarlomethods. WhilesomeoftheeffortshavedealtdirectlywithMarkov
chains on these integer-valued matrices, much recent success, including [11] [21], has
CONVERGENCE RATES OF GIBBS SAMPLERS 3
involved using knowledge of Gibbs samplers on convex sets in Rn and clever ways to
project from the continuous chain to the desired matrices [20].
Unfortunately, while the general bounds are polynomial in the number of entries
in the desired matrix, they often have a large degree and leading coefficient; see
[17]. In this paper, we find some better bounds for very specific cases. Like the
paper [26], this is part of an attempt to make further use of non-Markovian coupling
techniques [13] [2] [5] [19] and also to expand the small set of carefully analyzed Gibbs
samplers [22] [23] [7] [8]. In this case, the new techniques are two slight modifications
of the path-coupling method introduced in [4]. In many path-coupling arguments, a
burn-in argument is used to show that for most pairs of points in a metric space, there
is a path along which the Markov transition kernel is contractive acting on any pair
of points along the path. In this argument, we show that for all paths, the kernel is
contractive acting on most pairs of points along the path. This type of modification
seems likely to be useful only on continuous spaces.
We consider the following Gibbs sampler X [i,j] on the space M = {X ∈ R2n :
t n
(cid:80)n X[i,j] = n ∀ 1 ≤ j ≤ 2,(cid:80)2 X[i,j] = 2 ∀ 1 ≤ i ≤ n,X[i,j] ≥ 0} of nonneg-
i=1 j=1
ative n by 2 matrices with column sums fixed to be n and row sums fixed to be 2.
To make a step of the Gibbs sampler, choose two distinct integers 1 ≤ i < j ≤ n and
update the four entries X [i,1], X [i,2], X [j,1] and X [j,2] to be uniform
t+1 t+1 t+1 t+1
conditional on all other entries of X . Let U be the uniform distribution on M
t n n
inherited from Lebesgue measure. Then we find the following reasonable bound on
the mixing time of this sampler:
Theorem 2 (Convergence Rate for Narrow Matrices). For T > (31k +81)nlog(n),
||L(X )−U || ≤ 13n−k
T n TV
and conversely for T < (1−k)nlog(n), and n sufficiently large,
||L(X )−U || ≥ 1−2n−k
T n TV
3. General Strategy and the Partition Process
Both of our bounds will be obtained using a similar strategy, ultimately built
on the classical coupling lemma. We recall that a coupling of Markov chains with
transition kernel K is a process (X ,Y ) so that marginally both X and Y are Markov
t t t t
chains with transition kernel K. Although we always couple entire paths {X }T and
t t=0
{Y }T , we often use the shorthand notation of saying that we are coupling X and Y .
t t=0 t t
In order to describe a coupling, note that for both walks being studied, the evolution
of the Markov chain X can be represented by X = f(X ,i(t),j(t),λ(t)), where f is
t t+1 t
a deterministic function, i(t),j(t) are random coordinates (either elements of [n] or of
a group G), and λ(t) is drawn from Lebesgue measure on [0,1]. These representations
are given in equations (8) and (1) respectively. To couple X and Y , it is thus enough
t t
4 AARON SMITH
to couple the update variables i(t)α,j(t)α,λ(t)α, with α ∈ {x,y}, used to construct
X and Y respectively.
t t
Our couplings will provide bounds on mixing times through the following lemma
(see [15], Theorem 5.2 - they work in discrete space, but their proof doesn’t rely on
this assumption):
Lemma 3 (Fundamental Coupling Lemma). If (X ,Y ) is a coupling of Markov
t t
chains, Y is distributed according to the stationary distribution of K, and τ is the
0
first time at which X = Y , then
t t
||L(X )−L(Y )|| ≤ P[τ > t]
t t TV
In each chain, then, we begin with X started at a distribution of our choice, and Y
t t
started at stationarity. For any fixed (large) T, we will then couple X and Y so that
t t
they will have coupled by time T with high probability. Each coupling will have two
phases: an initial phase from time 0 to time T in which X and Y get close with high
1 t t
probability, and a non-Markovian coupling phase from time T to time T = T +T
1 1 2
in which they are forced to collide. Unlike many coupling proofs, the time of interest
T must be specified before constructing the coupling.
While the initial contraction phases are quite different for the two chains, the
final coupling phase can be described in a unified way. The unifying device is the
partition process P on set partitions of [n] = {1,2,...,n}, introduced in [26] for
t
a special case of the first sampler treated here (see that paper for details). This
partition process contains some information about the coordinates {i(t),j(t)}T
t=T1
used by Y throughout the entire process, and is the only source of information from
t
the future that is used to construct the non-Markovian coupling. Critically, we don’t
useanyinformationabouttherandomvariablesλ(t)usedateachstep, whichmakesit
trivial to check that the couplings constructed in this paper have the correct marginal
distributions.
The process {P }T will consist of a set of nested partitions of [n], P ≤ P ≤
t t=T1 T1 T1+1
... ≤ P = {{1},{2},...,{n}}, where we say partition A is less than partition B if
T
every element of partition B is a subset of an element of partition A. To construct P ,
t
wefirstlookatthesequenceofgraphsG withvertexset[n]andedgeset{(i(s),j(s)) :
t
s > t}. ThenletP consistoftheconnectedcomponentsofG . WhileconstructingP ,
t t t
we will also record a series of ‘marked times’ T = t > t > ... > t and associated
1 2 m
special subsets S(t ,1) and S(t ,2) of [n]. We will set t = T, and then inductively set
j j 1
t = sup{t : t < t ,P (cid:54)= P }. Finally, note that if P (cid:54)= P , the only difference
j j−1 t−1 t t−1 t
between them is that two elements of P have been merged into a single element in
t
P . Label the set merged at time t with fewer elements S(t ,1), and label the other
t−1 j j
one S(t ,2). If both sets have the same number of elements, set S(t ,1) to be the one
j j
containing the smallest element (this is, of course, quite arbitrary).
We will be interested in the smallest time τ such that P = [n], a single block.
T−τ
From classical arguments (see e.g. chapter 7 of [3]),
CONVERGENCE RATES OF GIBBS SAMPLERS 5
Lemma 4 (Connectedness). For the Gibbs sampler on narrow matrices,
1
P[τ > ( +(cid:15))nlog(n)] ≤ 2n−(cid:15)
2
The analogous lemma for the other example will be proved in Section 5, Lemma 7.
For both of our walks, we will use two types of coupling, the ‘proportional’ coupling
and the ‘subset’ coupling. In both cases, we will set i(t)x = i(t)y and j(t)x = j(t)y at
each step. In the proportional coupling, we will also set λ(t)x = λ(t)y.
To discuss the subset coupling, we must define the weight of X on a subset S ⊂ [n],
t
(cid:80)
which we call w(X ,S). For the simplex walk, we define w(X ,S) = X [s]. For
t t s∈S t
(cid:80)
narrowmatrices, wedefinew(X ,S) = X [s,1]. Thesubsetcouplingsassociated
t s∈S t
with subset S ⊂ [n], which are defined immediately prior to lemmas 18 and 8, will
often set w(X ,S) = w(Y ,S). We say that a subset coupling of subset S at time
t+1 t+1
t succeeds if that equality holds; otherwise, we say it fails.
In each case, the coupling of X and Y during the non-Markovian coupling phase
t t
will be as follows. At marked times t , we will perform a subset coupling of X , Y
j tj tj
with respect to S(t ,1). At all other times, we will perform a proportional coupling.
j
This leads to:
Lemma 5 (Final Coupling). Assume the non-Markovian coupling phase lasts from
time T to T, that P = {[n]}, and that all subset couplings succeed. Then X = Y .
1 T1 T T
Proof. Let F denote the collection of equations w(X ,S) = w(Y ,S) for all S ∈ P .
t t t t
We will show by induction that the equations F hold for all T ≤ t ≤ T. At time T ,
t 1 1
wehavew(X ,[n]) = w(Y ,[n]) = 1. Bydefinitionofthepartitionprocess, iftisnot
T1 T1
a marked time and all equations F hold, then all equations F also hold. In fact,
t t+1
this is true for any coupling of λ(t)x,λ(th)y at that step, not just the proportional
coupling.
Assume t = t is a marked time, and that the equations F hold. Then if F
j tj tj+1
don’t all hold, we must have that either w(X ,S(t ,1)) (cid:54)= w(Y ,S(t ,1)) or
tj+1 j tj+1 j
w(X ,S(t ,2)) (cid:54)= w(Y ,S(t ,2)), since none of the terms in the other equations
tj+1 j tj+1 j
change. By assumption, all subset couplings have succeeded, so w(X ,S(t ,1)) =
tj+1 j
w(Y ,S(t ,1)). By construction, w(X ,S(t ,2)) = w(X ,S(t ,1)∪S(t ,2))−
tj+1 j tj+1 j tj+1 j j
w(Y ,S(t ,1)) and similarly for Y , so w(X ,S(t ,2)) = w(Y ,S(t ,2)).
tj+1 j tj+1 tj+1 j tj+1 j
Thus, the inductive claim has been proved.
Finally, we note that if w(X ,{i}) = w(Y ,{i}) for any singleton {i}, then X [i] =
t t t
Y [i] for the sampler on the simplex (respectively X [i,j] = Y [i,j] for j ∈ {1,2} for
t t t
the other sampler). Since P = {{1},{2},...,{n}}, this proves the lemma. (cid:4)
T
So, in both cases, showing that all subset couplings succeed with high probability
is sufficient to show that coupling has succeeded.
6 AARON SMITH
4. Contraction for Gibbs Samplers on the Simplex with Geometry
In this section, we prove a contraction lemma for Gibbs samplers on the simplex
associated with a group G and symmetric generating set R of G (that is, R−1 = R),
where |G| = n, |R| = m, and id is the identity element of G. We recall briefly some
definitions. We write ∆ = {X ∈ RG|X[g] ≥ 0,(cid:80) X[g] = 1}. If X ∈ ∆ is a
G g∈G t G
copy of the Markov chain, we take a step by choosing g ∈ G, r ∈ R and λ ∈ [0,1]
uniformly and setting X [g] = λ(X [g]+X [gr]), X [gr] = (1−λ)X [g]+X [gr]),
t+1 t t t+1 t t
and for all other entries X [h] = X [h]. This walk is closely related to a slow simple
t+1 t
random walk on the group. In particular, we let Z ∈ G be the random walk that
t
evolves by choosing at each time step a group element g ∈ G and generator r ∈ R
uniformly at random, and setting Z = Z r if Z = g, and Z = Z otherwise.
t+1 t t t+1 t
Let K(cid:98) be the transition kernel associated with the random walk Z . Since R is
t
symmetric, therandomwalkisreversible, soK(cid:98) canbewrittenin abasisoforthogonal
eigenvectors with real eigenvalues 1 = λ(cid:98) > λ(cid:98) ≥ ... ≥ λ(cid:99) ≥ −1. Since it is 1-lazy,
1 2 n 2
all eigenvalues are in fact nonnegative. Let γ = 1−λ(cid:98) be the spectral gap of K(cid:98). In
(cid:98) 2
this section we will show that
Lemma 6 (Contraction Estimate for Gibbs Sampler on Cayley Graphs). Let X , Y
t t
be two copies of the Gibbs sampler on the simplex associated with G and R, with joint
distribution given by a proportional coupling at each step. Then
E[||X −Y ||2] ≤ 4ne−(cid:98)tγ(cid:98)(cid:99)
t t 2 8
Proof. We will construct an auxilliary Markov chain on G associated with X , and
t
compare it to the standard random walk Z . Let X , Y be two copies of the walk,
t t t
and couple them at each step with the proportional coupling. For h ∈ G, let Sh =
t
(cid:80)
(X [g] − Y [g])(X [hg] − Y [hg]). We will analyze the evolution of the vector
g∈G t t t t
S = (Sid,...).
t t
CONVERGENCE RATES OF GIBBS SAMPLERS 7
There are three cases to analyze: h ∈ R, h ∈/ R and h (cid:54)= id, and h = id. Let F be
t
the σ-algebra generated by X and Y , 0 ≤ s ≤ t. For case 1, we have
s s
4 2
E[Sh |F ] = (1− + )Sh
t+1 t n mn t
1 (cid:88) (cid:88)
+ [(X [i]+X [ri]−Y [i]−Y [ri])(X [hi]−Y [hi])
t t t t t t
2mn
i∈G r∈R,r(cid:54)=h,h−1
+(X [ri]+X [i]−Y [ri]−Y [i])(X [hri]−Y [hri])
t t t t t t
+(X [h−1i]−Y [h−1i])(X [i]+X [ri]−Y [i]−Y [ri])
t t t t t t
+(X [h−1ri]−Y [h−1ri])(X [i]+X [ri]−Y [i]−Y [ri])]
t t t t t t
2 (cid:88) 1
+ [ (X [i]+X [hi]−Y [i]−Y [hi])2
t t t t
mn 6
i∈G
+(X [i]−Y [i])(X [hi]−Y [hi])+(X [i]−Y [i])(X [h2i]−Y [h2i])]
t t t t t t t t
2 2 2 2
= (1− + )Sh + Sid + Sh2
n 3mn t 3mn t mn t
1 (cid:88)
+ (Shr−1 +Shr +Srh +Srh−1)
2mn t t t t
r∈R,r(cid:54)=h,h−1
and we note that the sum of the coefficients is 1− 2 . For case 2, we have
3mn
4 2m
E[Sh |F ] = (1− )Sh + Sh
t+1 t n t mn t
1 (cid:88)
+ (Shr−1 +Shr +Srh +Srh−1)
2mn t t t t
r∈R
2 1 (cid:88)
= (1− )Sh + (Shr−1 +Shr +Srh +Srh−1)
n t 2mn t t t t
r∈R
where the sum of the coefficients is 1. Finally, in case 3, we have
2 2 (cid:88)(cid:88)
E[Sid |F ] = (1− )Sid + (X [i]+X [ri]−Y [i]−Y [ri])2
t+1 t n t 3mn t t t t
r∈R i∈G
2 4 (cid:88)
= (1− )Sid + Sr
3n t 3mn t
r∈R
8 AARON SMITH
and here the sum of the coefficients is 1+ 2 . If we rewrite Uid = 1Sid, and otherwise
3n t 2 t
Ug = Sg, then we find the following transformations. For case 1, we have
t t
2 2 4 2
(2) E[Uh |F ] = (1− + )Uh + Uid + Uh2
t+1 t n 3mn t 3mn t mn t
1 (cid:88)
+ (Uhr−1 +Uhr +Urh +Urh−1)
2mn t t t t
r∈R,r(cid:54)=h,h−1
For case 2, we have
2 1 (cid:88)
(3) E[Uh |F ] = (1− )Uh + (Uhr−1 +Uhr +Urh +Urh−1)
t+1 t n t 2mn t t t t
r∈R
Finally, in case 3, we have
2 2 (cid:88)
(4) E[Uid |F ] = (1− )Uid + Ur
t+1 t 3n t 3mn t
r∈R
where the sum of the coefficients is now 1 in all three cases. In particular, the equa-
tions(2)to(4)nowdefineaMarkovchainonG. Fromequation(2), thisrandomwalk
sends the identity to itself with probability 1− 2 , and to a uniformly chosen element
3n
of S with the remaining probability; Equations (3) and (4) describe transitions for
h ∈ R and h ∈/ R respectively. Call the transition kernel K.
(cid:80)
Before analyzing the chain, we note that (X [i]−Y [i]) = 0, and so
i∈G t t
(cid:32) (cid:33)2
(cid:88)
0 = (X [i]−Y [i])
t t
i∈G
(cid:88) (cid:88)
= (X [i]−Y [i])2 + (X [i]−Y [i])(X [j]−Y [j])
t t t t t t
i∈G i(cid:54)=j
(cid:88)
= Sid + Sh
t t
h(cid:54)=id
From this calculation, if (cid:104)v,(2,1,1,...,1)(cid:105) = 0, then (cid:104)Kv,(2,1,1,...,1)(cid:105) = 0 as
well. By direct computation, π = 1 (2,1,1,...,1) is a reversible measure for K. It
n+1
is also clear that the distribution π = 1(1,1,...,1) is the reversible measure for K(cid:98).
(cid:98) n
We are now ready to compare the chains. Recall from [10] that the Dirichlet form
associated to a Markov chain with transition kernel Q and stationary distribution ν
is given by
1 (cid:88)
E(φ) = ν(g)Q(g,h)(φ(x)−φ(y))2
2
h,g∈G
Let E and E(cid:98) be the Dirichlet forms associated with K and K(cid:98) respectively. Then by
comparing terms, it is clear that E(φ) ≥ 1E(cid:98)(φ) for any φ and π, π(cid:98) ≤ 2. By e.g.
4 π π
Lemma 13.12 of [15], this implies γ ≥ 1γ. (cid:98)
8(cid:98)
CONVERGENCE RATES OF GIBBS SAMPLERS 9
Recall that if (cid:104)v,π(cid:105) = 0, then (cid:104)Kmv,π(cid:105) = 0 as well. In particular K applied to the
subspace orthogonal to π has L2 → L2 operator norm at most 1−γ. Thus, we have
for any v in that subspace
||Kmv|| ≤ e−(cid:98)γm(cid:99)||v||
2 2
going back to our original situation, we are interested in the vector (Sg). At time
t
0, Sid ≤ 2, and by Cauchy-Schwarz |Sh| ≤ 4. Thus, ||Ug|| ≤ 4n, and of course
0 0 0 2
|Sid| ≤ ||Sg|| ≤ ||Ug|| . So we find that
t t 2 t 2
E[|Sid|] ≤ 4ne−(cid:98)tγ(cid:98)(cid:99)
8
t
which is the contraction estimate in Lemma 6. (cid:4)
5. Coupling for Gibbs Samplers on the Simplex with Geometry
Having shown contraction, we must now show convergence in total variation dis-
tance. First, the analogue to Lemma 4:
Lemma 7 (Connectedness for Gibbs Sampler on Cayley Graphs). Let τ be as defined
immediately before Lemma 4 and let γ be as defined immediately before Theorem 1.
(cid:98)
Then for t > 8(C+3)log(n), we have
γ
(cid:98)
P[τ > t] ≤ 2n−C
Proof. We consider a graph-valued process G , where G is a graph with no edges,
t 0
and vertex set equal to the group G. To construct G from G , choose elements
t+1 t
g ∈ G and r ∈ R uniformly at random, and add the edge (g,gr) if it isn’t already
in G . We note that τ > t if and only if G is not connected, so we would like to
t t
estimate the time at which G becomes connected.
t
First, fix two elements x,y ∈ G. We’d like to see if x, y are in the same component
of G . To do so, let X , Y be two copies of the Gibbs sampler described in the last
t t t
section, with X = x, Y = y. Couple X , Y and G by the proportional coupling.
0 0 t t t
Then assume x, y are in different components C , C at time t. We would have
x y
(cid:88) (cid:88) 1 (cid:88) 1
|X [g]−Y [g]|2 ≥ +
t t |C |2 |C |2
x y
g g∈Cx g∈Cy
4
≥
n
By Markov’s inequality, then,
n (cid:88)
P[C (cid:54)= C ] ≤ E[ |X −Y |2]
x y t t
4
g
10 AARON SMITH
and so, by standard union bound for fixed x over all y, if A is the event that G is
t t
disconnected,
n2 (cid:88)
P[A ] ≤ supE[ |X −Y |2|X = µ,Y = ν]
t t t 0 0
4
µ,ν
g
≤ 2n3e−(cid:98)tγ(cid:98)(cid:99)
8
where the last inequality is due to Lemma 6. (cid:4)
Next, we define subset couplings and discuss success probabilities for this walk.
Fix points X , Y , subset S ⊂ [n] and updated coordinates i = i(t) ∈ S, j = j(t) ∈/ S.
t t
The next step is to construct a pair of uniform random variables λ = λ(t)x and
x
λ = λ(t)y with which to update the chains X and Y respectively. Assume first that
y t t
Xt[i]+Xt[j] < 1, and choose λ uniformly in [0,1]. Then set
Yt[i]+Yt[j] y
Y [i]+Y [j] 1 (cid:88)
t t
(5) λ = λ + (Y [s]−X [s])
x y t t
X [i]+X [j] X [i]+X [j]
t t t t
s∈S/{i}
if that results in a value between 0 and 1. Otherwise, choose λ independently of λ ,
x y
according to the density:
(cid:18) (cid:19)
X [i]+X [j]
t t
(6) f(λ) = C 1− 1 (λ)
g(λ)∈[0,1]
Y [i]+Y [j]
t t
where C−1 = (cid:82)1f(λ)dλ is a normalizing constant, and
0
Y [i]+Y [j] 1 (cid:88)
t t
g(λ) = λ + (Y [s]−X [s])
t t
X [i]+X [j] X [i]+X [j]
t t t t
s∈S/{i}
From the assumption that Xt[i]+Xt[j] < 1, it is easy to see that f really is a density
Yt[i]+Yt[j]
on [0,1]. From its construction as a remainder density, it is easy to check that under
this coupling, λ is uniformly distributed on [0,1]. If Xt[i]+Xt[j] > 1, an analogous
x Yt[i]+Yt[j]
construction will work. More precisely, in this case choose λ first, and then choose
x
λ to satisfy equation 5 if the result is in [0,1], rather than choosing λ first. If the
y y
result is not in [0,1], then choose λ according to its remainder measure, given by
y
equation (6) with X and Y flipped and g replaced by g−1. Note that if equation 5 is
t t
satisfied, then w(S,X ) = w(S,Y ).
t+1 t+1
For a pair of points (x,y) in the simplex, a pair of update entries (i,j), and a subset
S ⊂ [n] of interest such that i ∈ S and j not in S, we define p(x,y,i,j,S) to be the
probability that the associated subset coupling succeeds. Then the following lemma
from [26] gives a lower bound on this probability:
