Monte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMSN50)

Transcription

1 Monte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMSN50) Magnus Wiktorsson Centre for Mathematical Sciences Lund University, Sweden Lecture 5 Sequential Monte Carlo methods I January 30, 2018 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (1) 1 / 35

2 Plan of today s lecture Variance reduction reconsidered 1 Variance reduction reconsidered 2 3 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (2) 2 / 35

3 We are here Variance reduction reconsidered 1 Variance reduction reconsidered 2 3 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (3) 3 / 35

4 Last time: Variance reduction Last time we discussed how to reduce the variance of the standard MC sampler by introducing correlation between the variables of the sample. More specifically, we used 1 a control variate Y such that E(Y ) = m is known: Z = φ(x) + β(y m), where β was tuned optimally to β = C(φ(X), Y )/V(Y ). 2 antithetic variables V and V such that E(V ) = E(V ) = τ and C(V, V ) < 0: W = V + V. 2 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (4) 4 / 35

5 Last time: Variance reduction The following theorem turned out to be useful when constructing antithetic variables. Theorem Let V = ϕ(u), where ϕ : R R is a monotone function. Moreover, assume that there exists a non-increasing transform T : R R such that U = d. T (U). Then V = ϕ(u) and V = ϕ(t (U)) are identically distributed and C(V, V ) = C(ϕ(U), ϕ(t (U))) 0. An important application of this theorem is the following: Let F be a distribution function and φ a monotone function. Then, letting U U(0, 1), T (u) = 1 u, and ϕ(u) = φ(f 1 (u)) yields, for V = φ(f 1 (U)) and V = φ(f 1 (1 U)), V = d. V and C(V, V ) 0. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (5) 5 / 35

6 Last time: Variance reduction τ = 2 π/2 0 exp(cos 2 (x)) dx, V = 2 π 2 exp(cos2 (X)), V = 2 π 2 exp(sin2 (X)), W = V +V With Antithetic sampling Standard MC Sample size N V (= 2 N W ) M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (6) 6 / 35

7 Control variates reconsidered A problem with the control variate approach is that the optimal β, i.e. β = C(φ(X), Y ), V(Y ) is generally not known explicitly. Thus, it was suggested to 1 draw (X i ) N i=1, 2 draw (Y i ) N i=1, 3 estimate, via MC, β using the drawn samples, and 4 use this to optimally construct (Z i ) N i=1. This yields a so-called batch estimator of β. However, this procedure is computationally somewhat complex. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (7) 7 / 35

8 An online approach to optimal control variates The estimators def C N = 1 N def V N = 1 N N φ(x i )(Y i m) i=1 N (Y i m) 2 i=1 of C(φ(X), Y ) and V(Y ), respectively, can be implemented recursively according to and C l+1 = with C 0 = V 0 = 0. V l+1 = l l + 1 C l + 1 l + 1 φ(x l+1 )(Y l+1 m) l l + 1 V l + 1 l + 1 (Y l+1 m) 2. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (8) 8 / 35

9 An online approach to optimal control variates (cont.) Inspired by this we set for l = 0, 1, 2,..., N 1, Z l+1 = φ(x l+1 ) + β l (Y l+1 m), τ l+1 = l l + 1 τ l + 1 l + 1 Z l+1, ( ) def def def where β 0 = 1, β l = C l /V l for l > 0, and τ 0 = 0 yielding an online estimator. One may then establish the following convergence results. Theorem Let τ N be obtained through ( ). Then, as N, (i) τ N τ (a.s.), (ii) N(τ N τ) where σ 2 d. N (0, σ 2 ), def = V(φ(X)){1 ρ(φ(x), Y ) 2 } is the optimal variance. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (9) 9 / 35

10 Example: the tricky integral again We estimate τ = using π/2 π/2 π/2 exp(cos 2 (x)) dx sym = 2 0 π 2 exp(cos2 (x)) } {{ } =φ(x) Z = φ(x) + β (Y m), where Y = cos 2 (X) is a control variate with m = E(Y ) = π/2 0 2 π }{{} =f(x) dx = E f (φ(x)) cos 2 (x) 2 π dx = {use integration by parts} = 1 2. However, the optimal coefficient β is in general not known explicitly (tedious calculations give β = 4 e 1 2 π I 1 ( 1 2) ). M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (10) 10 / 35

11 Example: the tricky integral again cos2 cos(x).^2; phi 2(pi/2)*exp(cos2(x)); m = 1/2; X = (pi/2)*rand; Y = cos2(x); c = phi(x)*(y m); v = (Y m)^2; tau_cv = phi(x) + (Y m); beta = c/v; for k = 2:N, X = (pi/2)*rand; Y = cos2(x); Z = phi(x) + beta*(y m); tau_cv = (k 1)*tau_CV/k + Z/k; c = (k 1)*c/k + phi(x)*(y m)/k; v = (k 1)*v/k + (Y m)^2/k; beta = c/v; end M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (11) 11 / 35

12 Example: the tricky integral again τ Crude MC Adap CV Batch CV Exact β Adap CV Batch CV Exact Sample size N Sample size N M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (12) 12 / 35

13 We are here Variance reduction reconsidered 1 Variance reduction reconsidered 2 3 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (13) 13 / 35

14 We will now (and for the coming two lectures) extend the principal goal of the course to the problem of estimating sequentially sequences (τ n ) n 0 of expectations τ n = E fn (φ(x 0:n )) = φ(x 0:n )f n (x 0:n ) dx 0:n X n over spaces X n of increasing dimension, where again the densities (f n ) n 0 are known up to normalizing constants only; i.e. for every n 0, f n (x 0:n ) = z n(x 0:n ) c n, where c n is an unknown constant and z n is a known positive function on X n. As we will see, such sequences appear in many applications in statistics and numerical analysis. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (14) 14 / 35

15 We are here Variance reduction reconsidered 1 Variance reduction reconsidered 2 3 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (15) 15 / 35

16 We are here Variance reduction reconsidered 1 Variance reduction reconsidered 2 3 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (16) 16 / 35

17 A Markov chain on X R d is a family of random variables (= stochastic process) (X k ) k 0 taking values in X such that P(X k+1 B X 0, X 1,..., X k ) = P(X k+1 B X k ) for all B X. We call the chain time homogeneous if the conditional distribution of X k+1 given X k does not depend on k. The distribution of X k+1 given X k = x determines completely the dynamics of the process, and the density q of this distribution is called the transition density of (X k ). Consequently, P(X k+1 B X k = x k ) = q(x k+1 x k ) dx k+1. B M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (17) 17 / 35

18 Markov chains (cont.) Variance reduction reconsidered The following theorem provides the joint density f n (x 0, x 1,..., x n ) of X 0, X 1,..., X n. Theorem Let (X k ) be Markov with initial distribution χ. Then for n > 0, n 1 f n (x 0, x 1,..., x n ) = χ(x 0 ) q(x k+1 x k ). Corollary (Chapman-Kolmogorov equation) Let (X k ) be Markov. Then for n > 1, f n (x n x 0 ) = ( n 1 k=0 k=0 q(x k+1 x k ) ) dx 1 dx n 1. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (18) 18 / 35

19 Example: The AR(1) process As a first example we consider a first order autoregressive process (AR(1)) in R. Set X 0 = 0, X k+1 = αx k + ɛ k+1, where α is a constant and the variables (ɛ k ) k 1 of the noise sequence are i.i.d. with density function f. In this case, P(X k+1 x k+1 X k = x k ) = P(αX k + ɛ k+1 x k+1 X k = x k ) implying that q(x k+1 x k ) = = P(ɛ k+1 x k+1 αx k X k = x k ) = P(ɛ k+1 x k+1 αx k ), x k+1 P(X k+1 x k+1 X k = x k ) = x k+1 P(ɛ k+1 x k+1 αx k ) = f(x k+1 αx k ). M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (19) 19 / 35

20 We are here Variance reduction reconsidered 1 Variance reduction reconsidered 2 3 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (20) 20 / 35

21 Simulation of rare events for Markov chains Let (X k ) be a Markov chain on X = R and consider the rectangle B = B 0 B 1 B n R n, where every B l = (a l, b l ) is an interval. Here B can be a possibly extreme event. Say that we wish to to compute, sequentially as n increases, some expectation under the conditional distribution f n B of the states X 0:n = (X 0, X 1, X 2,..., X n ) given X 0:n B, i.e. τ n = E fn (φ(x 0:n ) X 0:n B) = E fn B (φ(x 0:n )) f(x 0:n ) = φ(x 0:n ) dx 0:n. B P(X 0:n B) }{{} =f n B (x 0:n )=z n(x 0:n )/c n Here the unknown probability c n = P(X 0:n B) of the rare event B is often the quantity of interest. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (21) 21 / 35

22 Simulation of rare events for Markov chains (cont.) As c n = P(X 0:n B) = 1 B (x 0:n )f(x 0:n ) dx 0:n a first naive approach could of course be to use standard MC and simply 1 simulate the Markov chain N times, yielding trajectories (X 0:n i ) N i=1, 2 count the number N B of trajectories that fall into B, and 3 estimate c n using the MC estimator c N n = N B N. Problem: if c n = 10 9 we may expect to produce a billion draws before obtaining a single draw belonging to B! As we will se, SMC methods solve the problem efficiently. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (22) 22 / 35

23 We are here Variance reduction reconsidered 1 Variance reduction reconsidered 2 3 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (23) 23 / 35

24 Estimation in general hidden Markov models (HMMs) A hidden Markov model (HMM) comprises two stochastic processes: 1 A Markov chain (X k ) k 0 with transition density q: X k+1 X k = x k q(x k+1 x k ). The Markov chain is not seen by us (hidden) but partially observed through 2 an observation process (Y k ) k 0 such that conditionally on the chain (X k ) k 0, (i) the Y k s are independent with (ii) conditional distribution of each Y k depending on the corresponding X k only. d. The density of the conditional distribution Y k (X k ) k 0 = Y k X k will be denoted by p(y k x k ). M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (24) 24 / 35

25 Estimation in general HMMs (cont.) Graphically: Y k 1 Y k Y k+1 (Observations)... X k 1 X k X k+1... (Markov chain) Y k X k = x k p(y k x k ) X k+1 X k = x k q(x k+1 x k ) X 0 χ(x 0 ) (Observation density) (Transition density) (Initial distribution) M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (25) 25 / 35

26 Example HMM: A stochastic volatility model The following dynamical system is used in financial economy (see Taylor (1982)). Let { Xk+1 = αx k + σɛ k+1, Y k = β exp ( Xk 2 ) ε k, where α (0, 1), σ > 0, and β > 0 are constants and (ɛ k ) k 1 and (ε k ) k 0 are sequences of i.i.d. standard normal-distributed noise variables. In this model the values of the observation process (Y k ) are observed daily log-returns (from e.g. the Swedish OMXS30 index) and the hidden chain (X k ) is the unobserved log-volatility (modeled by a stationary AR(1) process). The strength of this model is that it allows for volatility clustering, a phenomenon that is often observed in real financial time series. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (26) 26 / 35

27 Example HMM: A stochastic volatility model A realization of the the model looks like follows (here α = 0.975, σ = 0.16, and β = 0.63) Log returns Log volatility process log returns Days M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (27) 27 / 35

28 Example HMM: A stochastic volatility model Daily log-returns from the Swedish stock index OMXS30, from to y k k M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (28) 28 / 35

29 The smoothing distribution When operating on HMMs, one is most often interested in the smoothing distribution f n (x 0:n y 0:n ), i.e. the conditional distribution of a set X 0:n of hidden states given Y 0:n = y 0:n. Theorem (Smoothing distribution) f n (x 0:n y 0:n ) = χ(x 0)p(y 0 x 0 ) n k=1 p(y k x k )q(x k x k 1 ), L n (y 0:n ) where L n (y 0:n ) is the likelihood function given by L n (y 0:n ) = density of the observations y 0:n n = χ(x 0 )p(y 0 x 0 ) p(y k x k )q(x k x k 1 ) dx 0 dx n. k=1 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (29) 29 / 35

30 Estimation of smoothed expectations Being a high-dimensional (say n 1000 or 10, 000) integral over complicated integrands, L n (y 0:n ) is in general unknown. However by writing τ n = E(φ(X 0:n ) Y 0:n = y 0:n ) = φ(x 0:n )f n (x 0:n y 0:n ) dx 0 dx n with = φ(x 0:n ) z n(x 0:n ) c n dx 0 dx n, { z n (x 0:n ) = χ(x 0 )p(y 0 x 0 ) n k=1 p(y k x k )q(x k x k 1 ), c n = L n (y 0:n ), we may cast the problem of computing τ n into the framework of self-normalized IS. In particular we would like to update sequentially, in n, the approximation as new data (Y k ) appears. M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (30) 30 / 35

31 We are here Variance reduction reconsidered 1 Variance reduction reconsidered 2 3 M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (31) 31 / 35

32 Self-avoiding walks (SAWs) in the 2-dim integer (Z 2 ) lattice def Let S n = {x 0:n Z 2n : x 0 = 0, x k+1 x k = 1, x k x l, 0 l < k n} be the set of n-step self-avoiding walks in Z 2. 6 A self avoiding walk of length M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (32) 32 / 35

33 Self-avoiding walks (SAWs) in the honeycomb lattice (HCL) Let def S n = {x 0:n HCL : x 0 = 0, x k+1 x k = 1, x k x l, 0 l < k n} be the set of n-step self-avoiding walks in HCL. 0 Self-avoiding walk of length 50 in the HCL M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (33) 33 / 35

34 Application of SAWs Variance reduction reconsidered In addition, let SAWs are used in c n = S n = The number of possible SAWs of length n. polymer science for describing long chain polymers, with the self-avoidance condition modeling the excluded volume effect. statistical mechanics and the theory of critical phenomena in equilibrium. However, computing c n (and in analyzing how c n depends on n) is known to be a very challenging combinatorial problem! M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (34) 34 / 35

35 An MC approach to SAWs Trick: let f n (x 0:n ) be the uniform distribution on S n : f n (x 0:n ) = 1 1 Sn (x 0:n ), x c n }{{} 0:n Z 2n, =z(x 0:n ) We may thus cast the problem of computing the number c n (= the normalizing constant of f n ) into the framework of self-normalized IS based on some convenient instrumental distribution g n on Z 2n. In addition, solving this problem for n = 1, 2, 3,..., 508, 509,... calls for sequential implementation of IS. This will be the topic of HA2! M. Wiktorsson Monte Carlo and Empirical Methods for Stochastic Inference, L5 (35) 35 / 35