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Multiple-Try Metropolis. Jun Liu Department of Statistics Stanford University. Based on the joint work with F. Liang and W.H. Wong. The Basic Problems of Monte Carlo. Draw random variable Estimate the integral . Sometimes with unknown normalizing constant. c g( x ). c. u cg ( x ).

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jun liu department of statistics stanford university

Multiple-Try Metropolis

Jun Liu

Department of Statistics

Stanford University

Based on the joint work with F. Liang and W.H. Wong.

MCMC and Statistics

the basic problems of monte carlo
The Basic Problems of Monte Carlo
  • Draw random variable
  • Estimate the integral

Sometimes with unknown normalizing constant

MCMC and Statistics

how to sample from p x

c g(x)

c

u cg(x)

p(x)

x

How to Sample from p(x)
  • The Inversion Method.If U ~ Unif (0,1) then
  • The Rejection Method.
    • Generate x from g(x);
    • Draw u from unif(0,1);
    • Accept x if
    • The accepted x follows p(x).

The “envelope” distrn

MCMC and Statistics

high dimensional problems

a

High Dimensional Problems?

Ising Model

Partition

function

Metropolis Algorithm:

(a) pick a lattice point, say a, at random

(b) change current xa to 1- xa (so X(t) ® X*)

(c) compute r= p(X*)/ p(X(t) )

(d) make the acceptance/rejection decision.

MCMC and Statistics

slide5

General Metropolis-Hastings Recipe

  • Start with any X(0)=x0, and a “proposal chain” T(x,y)
  • Suppose X(t)=xt . At time t+1,
    • Draw y~T(xt ,y) (i.e., propose a move for the next step)
    • Compute the Metropolis ratio (or “goodness” ratio)
    • Acceptance/Rejection decision: Let

“Thinning

down”

MCMC and Statistics

slide6

Why Does It Work?

  • The detailed balance

Actual transition probability

from x to y, where

Transition probability

from y to x.

MCMC and Statistics

general markov chain simulation
General Markov Chain Simulation
  • Question: how to simulate from a target distribution p(X) via Markov chain?
  • Key: find a transition function A(X,Y) so that

f0 An ® p

that is, p is an invariant distribution of A.

  • Different from traditional Markov Chain theory.

MCMC and Statistics

slide8

Generally

If the actual transition probability is

I learnt it from Stein

where (x,y) is a symmetric function of x,y,

Then the chain has (x) as its invariant distribution.

MCMC and Statistics

slide9

Problems?

  • The moves are very “local”
  • Tend to be trapped in a local mode.

MCMC and Statistics

other approaches

Iteration t

xa

xc

Other Approaches?
  • Gibbs sampler/Heat Bath:better or worse?
  • Random directional search --- should be better if we can do it. “Hit-and-run.”
  • Adaptive directional sampling (ADS) (Gilks, Roberts and George, 1994).

Multiple

chains

MCMC and Statistics

gibbs sampler heat bath

A chosen direction

Gibbs Sampler/Heat Bath
  • Define a “neighborhood” structure N(x)
    • can be a line, a subspace, trace of a group, etc.
  • Sample from the conditional distribution.
  • Conditional Move

MCMC and Statistics

how to sample along a line
How to sample along a line?
  • What is the correct conditional distribution?
    • Random direction:
    • Directions chosen a priori: the same as above
    • In ADS?

MCMC and Statistics

the snooker theorem
The Snooker Theorem
  • Suppose x~and y is any point in the d-dim space. Let r=(x-y)/|x-y|.If t is drawn from

Then

follows the target distribution  .

If y is generated from distr’n, the new point x’ is indep. of y.

x

y (anchor)

MCMC and Statistics

connection with transformation group
Connection with transformation group
  • WLOG, we let y=0.
  • The move is now:x x’=tx

The set {t: t0} forms a transformation group.

Liu and Wu (1999) show that if t is drawn from

Then the move is invariant with respect to  .

MCMC and Statistics

another hurdle
Another Hurdle
  • How to draw from something like
  • Adaptive rejection? Approximation? Griddy Gibbs?
  • M-H Independence Sampler(Hastings, 1970)
    • need to draw from something that is close enough to p(x).

MCMC and Statistics

slide16

Ideas

  • Propose bigger jumps
    • may be rejected too often
  • Proposal with mix-sized stepsizes.
  • Try multiple times and select good one(s) (“bridging effect”) (Frankel & Smit, 1996)
  • Is it still a valid MCMC algorithm?

MCMC and Statistics

slide17

Multiple-Try Metropolis

Current is at x

Can be dependent ones

  • Draw y1,…,yk from the proposal T(x, y) .
  • Select Y=yjwith probability (yj)T(yj,x).
  • Draw from T(Y, x). Let
  • Accept the proposed yj with probability

MCMC and Statistics

a modification
A Modification
  • If T(x,y) is symmetric, we can have a different rejection probability:

Ref: Frankel and Smit (1996)

MCMC and Statistics

back to the example
Back to the example

Random Ray Monte Carlo:

y3

  • Propose random direction
  • Pick y from y1 ,…, y5
  • Correct for the MTM bias

y5

y4

x

y2

y1

MCMC and Statistics

an interesting twist

y2

y4

y6

y8

y1

y3

y5

y7

An Interesting Twist

x

  • One can choose multiple tries semi-deterministically.

Random equal grids

y

  • Pick y from y1 ,…, y8
  • The correction rule is the same:

MCMC and Statistics

use local optimization in mcmc
Use Local Optimization in MCMC
  • The ADS formulation is powerful, but its direction is too “random.”
  • How to make use of their framework?
    • Population of samples
    • Randomly select to be updated.
    • Use the rest to determine an “anchor point”
      • Here we can use local optimization techniques;
  • Use MTM to draw sample along the line, with the help of the Snooker Theorem.

MCMC and Statistics

slide22

Distribution contour

xc

xa

(anchor point)

A gradient or conjugate

gradient direction.

MCMC and Statistics

numerical examples
Numerical Examples
  • An easy multimodal problem

MCMC and Statistics

a more difficulttest example
A More DifficultTest Example
  • Mixture of 2 Gaussians:
  • MTM with CG can sample the distribution.
  • The Random-Ray also worked well.
  • The standard Metropolis cannot get across.

MCMC and Statistics

fitting a mixture model
Fitting a Mixture model
  • Likelihood:
  • Prior: uniform in all, but with constraints

And each group has at least one data point.

MCMC and Statistics

bayesian neural network training

y

Bayesian Neural Network Training

Nonlinear curve fitting:

  • Setting: Data =
  • 1-hidden layer feed-forward NN Model
  • Objective function for optimization:

MCMC and Statistics

slide29

Liang and Wong (1999) proposed a method that combines the snooker theorem, MTM, exchange MC, and genetic algorithm.

Activation function: tanh(z)

# hidden units M=2

MCMC and Statistics

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