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Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from classical inferential statistics that prohibits probability statements about parameters and is based on asymptotically sampling infinite samples from a theoretical population and finding parameter values that maximize the likelihood function. Mostly notorious is null-hypothesis significance testing (NHST) based on p-values. Bayesian statistics incorporate uncertainty (and prior knowledge) by allowing probability statements about parameters.
Features
- Julia is a fast dynamic-typed language that just-in-time (JIT) compiles into native code using LLVM
- It "runs like C but reads like Python", meaning that is blazing fast, easy to prototype and to read/write code
- It is multi-paradigm, combining features of imperative, functional, and object-oriented programming
- Julia documentation is a very friendly and well-written resource that explains the basic design and functionality of the language
- Open source and open access book on how to do Data Science using Julia
- Turing is an ecosystem of Julia packages for Bayesian Inference using probabilistic programming
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