3. Brounian motion f(O)eR+ is a special case of a Gaussian process with mean zero and covariance C(s, t) = min(s, t) (a) What is the distribution of f(1), the Brownian motion at time t = 4? (Hint...
3. Brounian motion f(O)eR+ is a special case of a Gaussian process with mean zero and covariance C(s, t) = min(s, t) (a) What is the distribution of f(1), the Brownian motion at time t = 4? (Hint: it may be useful to function recall that for any random variable X, var(X)-(x, X) (b) Fix tE R. What is the distribution of f(t)? (c) What is the distribution of f(4)-f(2)? (Hint: it may be useful to utilize var(X-Y) = var(X) + var(Y)-200v(X, Y).) (d) What is the distribution of f(t) f(s), assumingt? Note that your answer only depends on the distancet-s and not on the values of t and s themselves. This implies that Brownian motion has stationary increments.
3. Brounian motion f(O)eR+ is a special case of a Gaussian process with mean zero and covariance C(s, t) = min(s, t) (a) What is the distribution of f(1), the Brownian motion at time t = 4? (Hint: it may be useful to function recall that for any random variable X, var(X)-(x, X) (b) Fix tE R. What is the distribution of f(t)? (c) What is the distribution of f(4)-f(2)? (Hint: it may be useful to utilize var(X-Y) = var(X) + var(Y)-200v(X, Y).) (d) What is the distribution of f(t) f(s), assumingt? Note that your answer only depends on the distancet-s and not on the values of t and s themselves. This implies that Brownian motion has stationary increments.