(a) preliminary ingredients 1.0/2 points (graded) in a bayesian model, given the description of the situation, what is the prior distribution of the parameter of interest λ , which we define as probability that the coin chosen lands heads? enter the prior probabilities for λ=0.2 , λ=0.4 , λ=0.6 , λ=0.8 , as a vector (p(λ=0.2)p(λ=0.4)p(λ=0.6)p(λ=0.8)) . for example, if p(λ=0.2)=0.5,p(λ=0.4)=0.1,p(λ=0.6)= 0.1,p(λ=0.8)=0.3, then enter [0.5,0.1,0.1,0.3]. note the components are separated by commas, and the vector is enclosed by square brackets. (p(λ=0.2)p(λ=0.4)p(λ=0.6)p(λ=0.8))= correct [0.2,0.4,0.2,0.2] let the observations be x1 , x2 , ⋯ , xn , which are modelled as bernoulli random variables indicating whether a head was tossed on each of the n tosses. find a general expression for the likelihood function ln( x1 , x2 , ⋯ , xn |λ) in terms of λ , n , and ∑i=1nxi . (enter sigma_i(x_i) for ∑i=1nxi . do not worry if the parser does not render properly; the grader works independently. if you wish to have proper rendering, enclose sigma_i(x_i) by brackets. ) for this problem, write your answer in proportionality notation such that when λ=0.5 , the value of the likelihood function is 0.5n regardless of the value of the xi 's.