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2.3. COMPONENTS OF THE MODEL 41not know about. Such checks are an inherently creative enterprise, le to the analyst and thescientific community. Golems are very bad at it.Near the end of the chapter, you’ll see some examples of such checks. For now, note thatthe goal is not to test the truth value of the model’s assumptions. We know the model’s assumptionsare never exactly right, in the sense of matching the true data generating process.erefore there’s no point in checking if the model is true. Failure to conclude that a modelis false must be a failure of our imagination, not a success of the model. Moreover, modelsdo not need to be exactly true in order to produce highly precise and useful inferences. Allmanner of small world assumptions about error distributions and the like can be violated inthe large world, but a model may still produce a perfectly useful estimate. is is becausemodels are essentially information processing machines, and there are some surprising aspectsof information that cannot be easily captured by framing the problem in terms of thetruth of assumptions. 36Instead, the objective is to check the model’s adequacy for some purpose. is usuallymeans asking and answering additional questions, beyond those that originally constructedthe model. Both the questions and answers will depend upon the scientific context. So it’shard to provide general advice. ere will be many examples, throughout the book, andof course the scientific literature is replete with evaluations of the suitability of models fordifferent jobs—prediction, comprehension, measurement, and persuasion.2.3. Components of the modelNow that you’ve seen how the Bayesian model behaves, when confronted with the globetossing data, it’s time to open up the machine and learn how it works. Bayesian models allshare a common structure, comprising (1) a likelihood, (2) one or more parameters, and (3)a prior. ese components are usually chosen in that order. In this section, you’ll meet thesecomponents in some detail.2.3.1. Likelihood. e first and most influential component of a Bayesian model is theLIKELIHOOD. e likelihood is a mathematical formula that specifies the plausibility of thedata. You can build your own likelihood formula from basic assumptions of your story forhow the data arise, or you can use one of several off-the-shelf likelihoods that are commonin the sciences. Either way, the likelihood needs to tell you the probability of any possibleobservation, for any given state of the (small) world.In the case of the globe tossing model, the likelihood can be deduced from the data story.Begin by nominating all of the possible events. ere are two: water (W) and land (L). ereare no other events. e globe never gets stuck to the ceiling, for example. When we observea sample of W’s and L’s of length N (9 in the actual sample), we need to say how likely thatexact sample is, out of the universe of potential samples of the same length. at might soundchallenging, but it’s the kind of thing you get good at very quickly, once you start practicing.In this case, once we add our assumptions that (1) every toss is independent of the othertosses and (2) the probability of W is the same on every toss, probability theory providesa unique answer, known as the binomial distribution. is is the common “coin tossing”distribution. And so the probability of observing w W’s in n tosses, with a probability p ofW, is:n!Pr(w|n, p) =w!(n − w)! pw (1 − p) n−w .Read the above as: