The use of formal statistical methods to analyse quantitative data in data science has increased considerably over the last few years. One such approach, **Bayesian Decision Theory (BDT)**, also known as Bayesian Hypothesis Testing and Bayesian inference, is a fundamental statistical approach that quantifies the tradeoffs between various decisions using distributions and costs that accompany such decisions. In pattern recognition it is used for designing classifiers making the assumption that the problem is posed in probabilistic terms, and that all of the relevant probability values are known. Generally, we don’t have such perfect information but it is a good place to start when studying machine learning, statistical inference, and detection theory in signal processing. BDT also has many applications in science, engineering, and medicine.

From the perspective of BDT, any kind of probability distribution – such as the distribution for tomorrow’s weather – represents a prior distribution. That is, it represents how we expect today the weather is going to be tomorrow. This contrasts with frequentist inference, the classical probability interpretation, where conclusions about an experiment are drawn from a set of repetitions of such experience, each producing statistically independent results. For a frequentist, a probability function would be a simple distribution function with no special meaning.

*Y*come from. For instance, in image analysis you may want to decide if a picture is of a cat or a dog, in medicine you want to see if heart beat is nominal or irregular, or in radar may want to decide if a target is on the map or not. We assume two possible hypotheses (null hypothesis) and