Can you help me with this question. It is taken from Statistical Inference, Berger and Casella.

(d) Use this model to find this you have all the data and then assuming that $1 = 3040 18 Illloo 7.30 The EM algorithm is useful in a variety of situation, and the definition of ."miss- ing data" can be stretched to accommodate many different models. Suppose that we have a mixture density pf(x) + (1 - p)g(x), where p is unknown. If we observe X = (X1,..., Xn), the sample density is es Vid ads to tumil ads tadt wod2 (d TL IIbpf(z,) + (1 -p)g(.zi)], 10 moltuloe Josib A (5 luegads tadd wod i=1 which could be difficult to deal with. (Actually, a mixture of two is not terrible, but consider what the likelihood would look like with a mixture >._, pifi(x) for large *.) The EM solution is to augment the observed (or incomplete) data with Z = (Z1, . .". Zn), where Zi tells which component of the mixture X, came from; that is, Xilzi = 1~f(xi) and Xi|zi =0 ~ g(xi), and P(Zi = 1) = p. sJet2 doY weill ni eesza to tedmum