Can someone please explain the question above? Is it even in cs2 syllabus? If it is can you point it out in the core reading? Many thanks
Something very similar was asked in Sept 2019 (Q8). So, yes, it's examinable. The question relies on knowledge of statistics from CS1/university. CS2 is not a stand-alone course. If the value of X(t-1) is known, then Xt is a linear transformation of a normal RV, so is itself normal. Any likelihood function is the probability/PDF of getting the data we've observed. Here: L = f(x1,x2,...xn) Since X(t) depends on X(t-1), but not on any earlier values: L = f(x1)f(x2|x1)f(x3|x2)...f(xn|x(n-1))
