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A basic introduction to Bayesian Statistics

Bayesian Statistics is one of the theories in the discipline of statistics which is solely based on the idea that probability is not a fixed value to be calculated instead it is a value which shows belief in the happening of a certain event which can also change according to the addition of any new relevant information added to the mix. The Bayesian interpretation can be understood more easily if you have a basic knowledge about the topics like parameter learning, design of experiments, applied Bayesian statistics probability, factorization definition, categorical data in Bayesian analysis, local Markov property, Gibbs sampler, and the Metropolis-Hastings algorithm.

The probability of the outcome can be based on the basis of past experience and data about the event or you can also base it on your own personal views and beliefs. This interpretation of the probability is very different from all other theories still it plays an important role in solving real-world problems in various ways. Therefore, it is better to learn about Bayesian statistics and related concepts like developing Bayesian networks, conditional probabilities, and sampling, Markov blanket, multivariate normal distribution, causal networks, posterior distribution and analysis, structure learning and Bayesian inference.

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  • Summarizing posterior distributions
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  • Inferring unobserved variables

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