Category: R

Donsker’s invariance principle is a classical extension of the central limit theorem. It says that the path of a discrete random walk converges, if properly rescaled, in distribution to a standard Brownian motion. More details can be found in your favorite text book or on wikipedia. Here just comes an animation.

library(dplyr)
library(gganimate)
library(transformr)
set.seed(42)
n <- 10000  # maximal steps of random walk  
n_seq <- seq(from=10, 100, by = 10)  # starting phase
n_seq <- c(n_seq, seq(from=100, n, by = 100))
number_iteration <-length(n_seq)   
X <- sample(c(-1,1), n, replace=TRUE, prob=c(0.5, 0.5))
S <- cumsum(X)  # random walk S_n

W <- numeric(number_iteration*n)   
id <- numeric(number_iteration*n)      
time <- numeric(number_iteration*n)   
for (i in 1:number_iteration){
  for (j in (1:n)){
    index <- floor(j/n * n_seq[i]) + 1
    W[j+(i-1)*n] <- (S[index] + ((j/n * n_seq[i]) -(index-1)) *( S[index+1]-S[index]))/ sqrt(n_seq[i])
  }
  id[(1+n*(i-1)):(n*i)] <- rep(n_seq[i],n)
  time[(1+n*(i-1)):(n*i)] <- (1:n)/n
}

rw <- data.frame(x = time,
                     y = W,
                     id =id)

p <- ggplot(rw, aes(x=x, y=y)) + 
  geom_line()+ 
  ylab(expression(W^{(n)}~ (x)))+
  transition_states(
    id,
    transition_length = 2,
    state_length = 1) +
  enter_fade() + 
  exit_shrink() +
  ease_aes('sine-in-out')+
  labs(title = "Donsker's invariance principle. n = {closest_state}") 

animate(p, nframes=number_iteration*2)

Hilbert spaces play a prominent role in various fields of mathematics. An orthonormal basis of such a space is called a Hilbert basis. The purpose of this blog is to illustrate a very clasic and basic Hilbert basis – the Haar basis.

Let be a Hilbert basis of equipped with the standard scalar product . Hence, every can be written in a unique way as

   

where and .

A classic Hilbert basis consists of Haar functions that are supported on . They are defined using the Haar wavelet:

   

The Haar basis consists then of rescaled (by ) versions of shifted by ,

   

together with the constant function . (Note: the constant function has to be added since we consider the interval and not ). In R this looks like:

haar_mother <- function(x){
   (x >0 & x <= 0.5) - (x >0.5 & x <= 1)
}
haar <- function(x,j,k){
  2^(j/2) * haar_mother(2^j*x-k)
}

Animation of the Haar basis

j_max <- 3    # maximal depth
n_max <- 10   # resolution of grid
df <- data.frame(x=numeric(),
                 y=numeric(),
                 id=numeric())

x <- (1: 2^n_max)/2^n_max
id<-1
for (j in 0:j_max){
  for (k  in 0:(2^j-1))
  {
    df_new<- data.frame(x=x,
                        y=haar(x,j,k),
                        id=id)
    df <- bind_rows(df, df_new)
    id <- id+1
  }
}

ggplot(df, aes(x=x, y=y)) + 
  geom_step()+
  transition_states(
    id,
    transition_length = 2,
    state_length = 5
  ) +
  labs(title = 'Illustration of Haar basis') +
  ease_aes('sine-in-out')

Approximation via Haar basis

Now, every function can be written as

   

The coefficients are called the Haar Wavelet coefficients. Let us calculate them in the discrete setting. We give us a mesh and values and some function :

j_max <- 12
n_max <- 13  # maximal resolution
delta <- 2^{-n_max}
x <- (1: 2^n_max)/2^n_max
y <- x* sin(1/x) 
alpha <- list()  # list of Haar coefficients
for (j in 0:j_max){
  alpha[[j+1]] <- rep(0, 2^j)
  for (k  in 0:(2^j-1))
  {
    alpha[[j+1]][k+1] <-sum(haar(x,j,k)*y)*delta  # approximation of scalar product
  }
  
}

y_approx <- rep(0, length(y))  # approximated values of y

#### Calculating the approximations for different values of j

df <- data.frame(x=numeric(),
                 y=numeric(),
                 id=numeric())


y_approx <-  mean(y) # this is the contribution of the constant function
for (j in 0:j_max){
  for (k  in 0:(2^j-1))
  {
    y_approx <- y_approx + alpha[[j+1]][k+1]*haar(x,j,k)
  }
  df_new <- data.frame(x=x,
                       y=y_approx,
                       id=j)
  df <- bind_rows(df, df_new)
}

Animation of convergence

ggplot(df, aes(x=x, y=y)) + 
  geom_step()+
  transition_states(
    id,
    transition_length = 2,
    state_length = 1
  ) +
  labs(title = "Haar basis approximation of x sin(1/x). Depth:  {closest_state}") +
  ease_aes('sine-in-out')

Brownian motion is a central object of probability theory. The idea of Lévy’s construction is to construct the Brownian motion step by step on finite sets

   

of dyadic points. As is dense in the Brownian motion is then obtained as the uniform limit of linear interpolation on .
It is pretty easy to illustrate this construction using R and the package gganimate. We use the same notation as in the proof of Wiener’s theorem given on page 23 in “Brownian motion” by Peter Mörters and Yuval Peres.

library(dplyr)
library(gganimate)
library(transformr)
set.seed(42)
n_max <- 14                # maximal number of steps
D <- (0: 2^n_max)/2^n_max   # this is in fact D_n_max
B <- list()  
Z <- rnorm(1)
B[[1]] <- c(0, Z/2 + rnorm(1), Z) 
for (n in 2:n_max){
  B[[n]] <- rep(0, 2^n+1)
  index_known <- seq(1,2^n+1, by=2) # indices where values are known from previous steps
  B[[n]][index_known] <- B[[n-1]]
  index_unknown <- seq(2, 2^n, by=2) # indices where values are not yet defined
  for (d in index_unknown){
    B[[n]][d] <- 0.5*(B[[n]][d-1]+B[[n]][d+1])+ rnorm(1)*2^(-(n+1)/2)
  }
}


## interpolation and transformation in a data.frame
df <- data.frame(time=numeric(),
                 value=numeric(),
                 id=numeric())

for (n in 1:n_max){
  D_n<-(0: 2^n)/2^n   
  B_interpol<- approx(D_n, B[[n]], xout = D)$y   # interpolation
  df_new <- data.frame(time=D,
                       value=B_interpol,
                       id=n)
  df <- bind_rows(df, df_new)
}

## animation
ggplot(df, aes(x=time, y=value)) + 
  geom_line()+
  transition_states(
    id,
    transition_length = 2,
    state_length = 1
  ) +
labs(title = 'Levy`s construction of Brownian motion.  Step: {closest_state}', x = 'time', y = 'position') +
ease_aes('sine-in-out')