Timothy Schorn | University of South Dakota (original) (raw)
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In Bayesian inference, uncertainty is expressed in terms of probability. More important, in Bayes... more In Bayesian inference, uncertainty is expressed in terms of probability. More important, in Bayesian inference, probability is subjective: it is belief. Bayesian inference starts when we formulate a model that we believe is a good representation of the situation that holds our interest. We then construct a distribution over the parameters of the model-which are unknown-where that distribution represents our prior beliefs about the situation before we observe data. According to Baye's Law, the posterior distribution for these parameters given the data we have observed is proportional to the product of our prior beliefs and the joint probability of the observed variables, given the parameters. These are the components of Bayesian inference: model, prior beliefs, data, and posterior beliefs.
Bustan: The Middle East Book Review, 2013
In Bayesian inference, uncertainty is expressed in terms of probability. More important, in Bayes... more In Bayesian inference, uncertainty is expressed in terms of probability. More important, in Bayesian inference, probability is subjective: it is belief. Bayesian inference starts when we formulate a model that we believe is a good representation of the situation that holds our interest. We then construct a distribution over the parameters of the model-which are unknown-where that distribution represents our prior beliefs about the situation before we observe data. According to Baye's Law, the posterior distribution for these parameters given the data we have observed is proportional to the product of our prior beliefs and the joint probability of the observed variables, given the parameters. These are the components of Bayesian inference: model, prior beliefs, data, and posterior beliefs.
Bustan: The Middle East Book Review, 2013