# Introduction to Markov Chain Monte Carlo Fall 2012 By Yaohang Li, Ph.D.

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Introduction to Markov Chain Monte Carlo Fall 2012 By Yaohang Li, Ph.D.

Review Last Class –Linear Operator Equations –Monte Carlo This Class –Markov Chain –Metropolis Method Next Class –Presentations

Markov Chain –Consider chains whose transitions occur at discrete times States S 1, S 2, … X t is the state that the chain is in at time t Conditional probability –P(X t =S j |X t1 =S i1, X t2 =S i2, …, X tn =S in ) –The system is a Markov Chain if the distribution of Xt is independent of all previous states except for its immediate predecessor Xt-1 –P(X t =S j |X 1 =S i1, X 2 =S i2, …, X t-1 =S it-1 )=P(X t =S j |X t-1 =S it-1 )

Characteristics of Markov Chain Irreducible Chain Aperiodic Chain Stationary Distribution –Markov Chain can gradually forget its initial state –eventually converge to a unique stationary distribution invariant distribution Ergodic average

Target Distribution Target Distribution Function –  (x)=ce -h(x) h(x) –in physics, the potential function –other system, the score function c –normalization constant »make sure the integral of  (x) is 1 Presumably, all pdfs can be written in this form

Metropolis Method Basic Idea –Evolving a Markov process to achieve the sampling of  Metropolis Algorithm –Start with any configuration x0, iterates the following two steps –Step 1: Propose a random “perturbation” of the current state, i.e., xt -> x’, where x’ can be seen as generated from a symmetric probability transition function Q(x t ->x’) i.e., Q(x->x’)=Q(x’->x) Calculate the change  h=h(x’)-h(x) –Step 2: Generate a random number u ~ U[0, 1). Let x t+1 =x’ if u<=e -  h, otherwise x t+1 =x t

Simple Example for Hard-Shell Balls Simulation –Uniformly distributed positions of K hard-shell balls in a box –These balls are assumed to have equal diameter d (X, Y) ={(xi, yi), i=1, …, K} –denote the positions of the balls –Target Distribution  (X, Y) = constant if the balls are all in the box and have no overlaps =0 otherwise Metropolis algorithm –(a) pick a ball at random, say, its position is (xi, yi) –(b) move it to a tentative position (xi’, yi’)=(xi+  1, yi+  2), where  1,  2 are normally distributed –(c) accept the proposal if it doesn’t violate the constraints, otherwise stay out

Simulation of K-Hardballs

Hastings’ Generalization Metropolis Method –A symmetric transition rule Q(x->x’)=Q(x’->x) Hastings’ Generalization –An arbitrary transition rule Q(x->x’) –Q() is called a proposal function

Metropolis-Hastings Method –Given current state x t –Draw U ~ U[0, 1) and update  (x, y) = min{1,  (y)Q(y->x)/(  (x)Q(x->y))}

Detailed Balance Condition Transition Kernel for the Metropolis-Hastings Algorithm –I(.) denotes the indicator function Taking the value 1 when its argument is true, and 0 otherwise –First term arises from acceptance of a candidate y=x t+1 –Second term arises from rejection, for all possible candidates y Detailed Balance Equation –  (x t )P(x t+1 |x t ) =  (x t+1 )P(x t |x t+1 )

Gibbs Sampling Gibbs Sampler –A special MCMC scheme –The underlying Markov Chain is constructed by using a sequence of conditional distributions which are so chosen that  is invariant with respect to each of these “conditional” moves Gibbs Sampling –Definition X -i ={X 1, …, X i-1, X i+1, … X n } –Proposal Distribution Updating the ith component of X Qi(X i ->Y i, X -i )=  (Y i |X -i )

MCMC Simulations Two Phases –Equilibration Try to reach equilibrium distribution Measurements of steps before equilibrium are discarded –Production After reaching equilibrium Measurements become meaningful Question –How fast the simulation can reach equilibrium?

Autocorrelations Given a time series of N measurements from a Markov process Estimator of the expectation value is Autocorrelation function –Definition where

Behavior of Autocorrelation Function Autocorrelation function –Asymptotic behavior for large t – is called (exponential) autocorrelation time –is related to the second largest eigenvalue of the transition matrix –Special case of the autocorrelations

Integrated Autocorrelation Time Variance of the estimator Integrated autocorrelation time When

Reaching Equilibrium How many steps to reach equilibrium?

Example of Autocorrelation Target Distribution

Small Step Size (d = 1)

Medium Step Size (d = 4)

Large Step Size (d = 8)

Microstate and Macrostate Macrostate –Characterized by the following fixed values N: number of particles V: volume E: total energy Microstate –Configuration that the specific macrostate (E, V, N) can be realized –accessible a microstate is accessible if its properties are consistent with the specified macrostate

Ensemble System of N particles characterized by macro variables: N, V, E –macro state refers to a set of these variables There are many micro states which give the same values of {N,V,E} or macro state. –micro states refers to points in phase space All these micro states constitute an ensemble

Microcanonical Ensemble Isolated System –N particles in volume V –Total energy is conserved –External influences can be ignored Microcanonical Ensemble –The set of all micro states corresponding to the macro state with value N,V,E is called the Microcanonical ensemble Generate Microcanonical Ensemble –Start with an initial micro state –Demon algorithm to produce the other micro states

The Demon Algorithm Extra degree of freedom the demon goes to every particle and exchanges energy with it Demon Algorithm –For each Monte Carlo step (for j=1 to mcs) –for i=1 to N Choose a particle at random and make a trial change in its position Compute  E, the change in the energy of the system due to the change If  E<=0, the system gives the amount |  E| to the demon, i.e., Ed=Ed-  E, and the trial configuration is accepted If  E>0 and the demon has sufficient energy for this change (Ed>=  E), the demon gives the necessary energy to the system, i.e., Ed=Ed-  E, and the trial configuration is accepted. Otherwise, the trial configuration is rejected and the configuration is not changed

Monte Carlo Step In the process of changing the micro state the program attempts to change the state of each particle. This is called a sweep or Monte Carlo Step per particle mcs In each mcs the demon tries to change the energy of each particle once. mcs provides a useful unit of ‘time’

MD and MC Molecular Dynamic –a system of many particles with E, V, and N fixed by integrating Newton’s equations of motion for each particle –time-averaged value of the physical quantities of interest Monte Carlo –Sampling the ensemble –ergodic-averaged value of the physical quantities of interest How can we know that the Monte Carlo simulation of the microcanonical ensemble yields results equivalent to the time- averaged results of molecular dynamics? –Ergodic hypothesis have not been shown identical in general have been found to yield equivalent results in all cases of interest

One-Dimensional Classical Ideal Gas Ideal Gas –The energy of a configuration is independent of the positions of the particles –The total energy is the sum of the kinetic energies of the individual particles Interesting physical quantity –velocity A Java Program of the 1-D Classical Ideal Gas –http://electron.physics.buffalo.edu/gonsalves/phy411- 506_spring01/Chapter16/feb23.htmlhttp://electron.physics.buffalo.edu/gonsalves/phy411- 506_spring01/Chapter16/feb23.html –Using the demon algorithm

Physical Interpretation of Demon Demon may be thought of as a thermometer Simple MC simulation of ideal gas shows –Mean demon energy is twice mean kinetic of gas The ideal gas and demon may be thought of –as a heat bath (the gas) characterized by temp T related to its average kinetic energy –and a sub or laboratory system (the demon) –the temperature of the demon is that of the bath The demon is in macro state T –the canonical ensemble of microstates are the E d

Canonical ensemble Normally system is not isolated. –surrounded by a much bigger system –exchanges energy with it. Composite system of laboratory system and surroundings may be consider isolated. Analogy: – lab system demon –surroundings ideal gas Surroundings has temperature T which also characterizes macro state of lab system

Boltzmann distribution In canonical ensemble the daemon’s energy fluctuates about a mean energy Probability that demon has energy E d is given by –Boltzmann distribution Proved in statistical mechanics Shown by output of demon energy Mean demon energy

Phase transitions Examples: –Gas - liquid, liquid - solid –magnets, pyroelectrics –superconductors, superfluids Below certain temperature T c the state of the system changes structure Characterized by order parameter –zero above T c and non zero below T c –e.g. magnetisation M in magnets, gap in superconductors

Ising model

Why Ising model? Simplest model which exhibit a phase transition in two or more dimensions Can be mapped to models of lattice gas and binary alloy. Exactly solvable in one and two dimensions No kinetic energy to complicate things Theoretically and computationally tractable –can make dedicated ‘Ising machine’

2-D Ising Model E=-J E=+J

Physical Quantity Energy –Average Energy –Mean Square Energy Fluctuation = - 2 Magnetization M –Given by –mean square magnetization = - 2 Temperature (Microcanonical)

Simulation of Ising Model Microcanonical Ensemble of Ising Model –Demon Algorithm Canonical Ensemble of Ising Model –Metropolis Algorithm

Metropolis for Ising

Simulating Infinite System –Periodic Boundaries

Energy Landscape –Complex biological or physical system has a complicated and rough energy landscape

Local Minima and Global Minima Local Minima –The physical system may transiently reside in the local minima Global Minima –The most stable physical/biological system –Usually the native configuration Goal of Global Optimization –Escape the traps of local minima –Converge to the global minimum

Local Optimization –Quickly reach a local minima –Approaches Gradient-based Method Quasi-Newton Method Powell Method Hill-climbing Method Simplex Global Optimization –Find the global minima –More difficult than local optimization

Consequences of the Occasional Ascents Help escaping the local optima. desired effect Might pass global optima after reaching it adverse effect (easy to avoid by keeping track of best-ever state)

Temperature is the Key Probability of energy changes as temperature raises

Boltzmann distribution At thermal equilibrium at temperature T, the Boltzmann distribution gives the relative probability that the system will occupy state A vs. state B as: where E(A) and E(B) are the energies associated with states A and B.

Real annealing: Sword He heats the metal, then slowly cools it as he hammers the blade into shape. –If he cools the blade too quickly the metal will form patches of different composition; –If the metal is cooled slowly while it is shaped, the constituent metals will form a uniform alloy.

Simulated Annealing Algorithm Idea: Escape local extrema by allowing “ bad moves, ” but gradually decrease their size and frequency. –proposed by Kirkpatrick et al. 1983 Simulated Annealing Algorithm –A modification of the Metropolis Algorithm Start with any configuration x0, iterates the following two steps Initialize T to a high temperature Step 1: Propose a random “perturbation” of the current state, i.e., xt -> x’, where x’ can be seen as generated from a symmetric probability transition function Q(x t ->x’) –i.e., Q(x->x’)=Q(x’->x) –Calculate the change  h=h(x’)-h(x) Step 2: Generate a random number u ~ U[0, 1). Let x t+1 =x’ if u<=e -  h/T, otherwise x t+1 =x t Step 3: Slightly reduce T and go to Step 1 Until T is your target temperature

Note on simulated annealing: limit cases Boltzmann distribution: accept “ bad move ” with  E<0 (goal is to maximize E) with probability P(  E) = exp(  E/T) If T is large:  E < 0  E/T < 0 and very small exp(  E/T) close to 1 accept bad move with high probability If T is near 0:  E < 0  E/T < 0 and very large exp(  E/T) close to 0 accept bad move with low probability Random walk Deterministic down-hill

Simulated Tempering Basic Idea –Allow the temperature to go up and down randomly –Proposed by Geyers 1993 –Can be applied to a very rough energy landscape Simulated Tempering Algorithm –At the current sampler i, update x using a Metropolis-Hastings update for  i(x). –Set j = i  1 according to probabilities pi,j, where p1,2 = pm,m-1 = 1.0 and pi,i+1 = pi,i-1 = 0.5 if 1 < i < m. –Calculate the equivalent of the Metropolis-Hastings ratio for the ST method, –where c(i) are tunable constants and accept the transition from i to j with probability min(rst, 1). The distribution  is called the pseudo-prior because the function ci(x)  i(x) resembles formally the product of likelihood and prior.

Parallel Tempering Basic Idea –Allow multiple walks at different temperature levels –Switch configuration between temperature levels Based on Metropolis-Hastings Ratio –Also called replica exchange method –Very powerful in complicated energy landscape

Multi-Transition Metropolis –Allow steps of different size –Proposed by J. Liu Basic Idea –Large step size can escape the deep local minima trap more easily –However, the acceptance rate is much lower

Accelerated Simulated Tempering Accelerated Simulated Tempering [Li, Protopopescu, Gorin, 2004] –A modified scheme of simulated tempering Allow larger transition steps at high temperature level –Large transition steps at high temperature level –Small transition steps at low temperature level Lean the ladder –Biased the transition probability between temperature levels –Favor low temperature level

Hybrid PT/SA Method Hybrid PT/SA Method [Li, Protopopescu, Nikita, Gorin, 2009] –System Setup Subsystem –x i at different temperature levels Composite System –Macrostate X = { x 1, x 2, …, x N } –Transition Types Parallel Local Transitions –Local Monte Carlo Moves at a specific temperature level –Acceptance Probability Replica Exchange –Replica Exchange between two randomly chosen temperature levels –Acceptance Probability –Cooling Scheme Examine the Macrostate for Equilibrium –Boltzmann Distribution of the Subsystems

Application of Global Optimization VLSI Floorplan Design VLSI Design –Goal: putting chips on the circuit floor without overlaps

Summary Markov Chain Markov Chain Monte Carlo Metropolis Method Hastings’ Generalization Detail Balance Condition Gibbs Sampler Autocorrelation Function and Integrated Autocorrelation Time

What I want you to do? Review Slides Review basic probability/statistics concepts Prepare for your presentation topic and term paper Work on your assignment 5

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