Ononot — If a likelihood function L(\theta) is proportional to \theta

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Inferential Statisticshard

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If a likelihood function $L(\theta)$ is proportional to $\theta^k (1-\theta)^{n-k}$ and the prior distribution is $\text{Beta}(\alpha, \beta)$ , what characterizes the posterior distribution $\pi(\theta|x)$ ?