Basis Lab

Markov Chains

Why the Chain Forgets Where It Started

Last timeThe Distribution the Rule Leaves Alone

Rule out two failures and a chain forgets its starting point from every start. The forgetting happens at a rate the rule itself fixes, and that rate is what a mixing time measures.

The previous lesson found a distribution that a hop leaves alone. It did not

show that a chain ever gets there, and the alternating chain is a reminder that

being left alone is no guarantee of being arrived at.

This lesson supplies the missing argument. Two conditions, both already built in

the third lesson, are enough: one closed class and no cycle. With those, every

starting point leads to the same place.

Two copies, converging

The clean way to see it is to stop watching one chain and watch two, started

differently, running under the same rule.

FIG 1The same rule from the best and worst starting points
started runningstarted waiting00.20.40.60.81010203040506070morningschance of running

both copies of the chance, added upunder 0.001% of it gone by the end, at every setting of every slider

2

70 of 70 steps

running, starting from runningrunning, starting from waitingthe gap between the two
stepped by
pa → 0.9*pa + (0.5 + k)*pb + 0.0*pc pb → 0.1*pa + 0.1*pb + 0.3*pc pc → 0.0*pa + (0.4 - k)*pb + 0.7*pc qa → 0.9*qa + (0.5 + k)*qb + 0.0*qc qb → 0.1*qa + 0.1*qb + 0.3*qc qc → 0.0*qa + (0.4 - k)*qb + 0.7*qc
each rule reads
the state as it was when the step began
started at
pa = 1.0, pb = 0.0, pc = 0.0, qa = 0.0, qb = 0.0, qc = 1.0
held steady
both copies of the chance, added up = pa + pb + pc + qa + qb + qc
drift
under 0.001% over the run, against 0.20% allowed
Two copies of the machine chain under one rule, one started certainly running and the other certainly waiting for a part. Each copy keeps its own chance adding to one, so the two together add to two, which is the checked quantity. The first copy falls, the second climbs, and they meet at the resting share and stay there. The third trace is the gap, and it is the thing to watch: it shrinks by very nearly the same ratio every morning once the first morning or two have passed. Push the slider and both copies move to a new resting share and the gap closes faster, because a chain with more ways of moving between its situations forgets its starting point sooner. The gap never crosses zero and never comes back.

The reason the gap shrinks is worth stating carefully, because it is the whole

proof and it is not an argument about averages.

After enough hops, every situation can be reached from every other with some

chance above nought. That is what one closed class and no cycle give you. So

both copies, whatever they started as, place at least some fixed share of their

chance in the same places in the same proportions.

That shared share is identical in the two copies. The rule treats it identically

in both, so it stays identical forever: it can never come apart again. Only the

remainder can still differ, and the same argument applies to the remainder next

time round.

So the difference between the copies is multiplied by some factor below one,

every round, forever. A number multiplied repeatedly by a factor below one goes

to nought, and the two copies therefore agree in the limit. Since one of the

copies could have been started at the resting distribution, where it stays

forever, every other start converges to it.

The lesson stops here

4 more paragraphs to go

You have read the opening. The rest of the argument, the problems that check whether it landed, and the lines worth keeping at the end all come with a plan.

The first lesson of every course in the library reads the whole way through, free, so you can see exactly what the rest of them are.

See the planThe contents

This is the reading half

Starting the course gives you your own copy of it. Every idea on every page has problems standing under it, marked with a reason rather than a tick, and any sentence you do not believe can be opened and argued with. None of that can happen on a page nobody owns.

The contents

The rest of this course

  1. 01Where You Are, and One Hop
  2. 02Where You Are After Many Hopsopening only
  3. 03What Is Reachable, and What Traps Youopening only
  4. 04The Distribution the Rule Leaves Aloneopening only
  5. 05Why the Chain Forgets Where It Startedyou are here
  6. 06How Long Until It Gets Thereopening only
  7. 07Chains That Look the Same Run Backwardsopening only
  8. 08Building a Chain to Reach a Chosen Distributionopening only

Read alongside