In order to motivate the idea of parameter estimation we need to first understand the notion of mathematical modeling. What is the idea behind modeling real world phenomena? Mathematically modeling an aspect of the real world enables us to better understand it and better explain it, and perhaps enables us to reproduce it, either on a large scale, or on a simplified scale that characterizes only the critical parts of that phenomenon. How do we model these real life phenomena? These real life phenomena are captured by means of distribution models, which are extracted or learned directly from data gathered about them. So, what do we mean by parameter estimation? Every distribution model has a set of parameters that need to be estimated. These parameters specify any constants appearing in the model and provide a mechanism for efficient and accurate use of data. … Bayesian Methods of Parameter Estimation

# Document worth reading: “Bayesian Methods of Parameter Estimation”

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