MC Integration

Classical Monte Carlo Integration

Robert and Casella (2013) introduces the classical Monte Carlo integration:

Toy Example

为了计算积分 我们通常采用MC模拟:

  1. 计算区域的volume:

  2. 近似:

根据大数律有

并且由中心极限定理有

其中

另外,注意到

I=Dg(x)dx=DVg(x)1Vdx=Ef[Vg(x)],I = \int_Dg(\mathbf x)d\mathbf x = \int_D Vg(\mathbf x)\frac 1V d\mathbf x = {\mathbb E}_f[Vg(\mathbf x)],

则可以直接利用上一节的结论。

## estimate pi
##
## I = \int H(x, y)dxdy
## where H(x, y) = 1 when x^2+y^2 <= 1;
## otherwise H(x, y) = 0
## volume
V = 4
n = 100000
x = runif(n, -1, 1)
y = runif(n, -1, 1)
H = x^2+y^2
H[H<=1] = 1
H[H>1] = 0
I = V* mean(H)
cat("I = ", I, "\n")
## n = 100, I = 2.96
## n = 1000, I = 3.22
## n = 10000, I = 3.1536
## n = 100000, I = 3.14504

Polar Simulation

Note: Unless otherwise stated, the algorithms and the corresponding screenshots are adopted from Robert and Casella (2013).

The following Julia program can be used to do polar simulation.

using Statistics
function polarsim(x::Array)
while true
phi1 = rand()*2*pi
phi2 = rand()*pi - pi/2
u = rand()
xdotxi = x[1]*cos(phi1) + x[2]*sin(phi1)*cos(phi2) + x[3]*sin(phi1)*sin(phi2)
if u <= exp(xdotxi - sum(x.^2)/2)
return(randn() + xdotxi)
end
end
end
# approximate E^pi
function Epi(m, x = [0.1, 1.2, -0.7])
rhos = ones(m)
for i = 1:m
rhos[i] = 1/(2*polarsim(x)^2+3)
end
return(mean(rhos))
end
# example
Epi(10)