[SOLVED] 代写 python parallel Bayesian The unparalleled benefit of Bayesian modeling is attributed to flexibility where a hierarchical model can come around. There are a range of model variants to analytical strategies. Imagine that you do research on response time for texting to your friends. Likely, the response time is subject to whom you will talk. The persons to whom you talked are denoted by k (=1,…,12).

The unparalleled benefit of Bayesian modeling is attributed to flexibility where a hierarchical model can come around. There are a range of model variants to analytical strategies. Imagine that you do research on response time for texting to your friends. Likely, the response time is subject to whom you will talk. The persons to whom you talked are denoted by k (=1,…,12).

(i) Estimate the time response to each person via Bayesian models (using any suitable prior density). Present the analycal results and computing codes (Python or R) for implementation.

(ii) Related to the problem (i) above, provide rationales of the selected prior distribution. (You may also support your choice with reasonable assumptions)

(iii) Suppose that common effects to each response time are present. We hypothesize that your response timeof conversation have similarities with one another. To this end, answer to the problem (i)-(ii) via hierarchical Bayesian models.