Consider coin toss experiment using bayesian analysis with beta prior: g(e) = tr)「(s) where θ represents the probability o heads. in an experiment n con tosses res it n k heads. based on this experiment andus ng ba es theorem e os e ir rot abl density for 8 is given by n rs) a) (2pt) plot prior and posterior distributions for all 4 cases defined by the following values for parameters: (r, s) (1,1),4,4) & (n,,,11) b) (2pt) in each one of these 4 cases, determine the probability that the coin is biased towards heads (ie p(e> 0.5) (you can use beta distribution integration functionality of r)