Category Archives: Bayesian

12 Bayesian Machine Learning Applications Examples

bayesian machine learning appplications examples

Bayesian machine learning is one of the most powerful tools in data analytics. Bayes’ theorem, which was first introduced by Reverend Thomas Bayes in 1764, provides a way to infer probabilities from observations. Bayesian machine learning has become increasingly popular because it can be used for real-world applications such as credit card fraud detection and spam filtering. In this blog post, we will discuss Bayesian machine learning real-world examples to help you understand how Bayes’ theorem works. Bayesian machine learning utilizes Bayes’ theorem to predict occurrences. Bayesian inference is grounded in Bayes’ theorem, which allows for accurate prediction when applied to real-world applications. Here are some great examples of real-world …

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Bayes Theorem Explained with Examples

In this post, you will learn about Bayes’ Theorem with the help of examples. It is of utmost importance to get a good understanding of Bayes Theorem in order to create probabilistic models. Bayes’ theorem is alternatively called as Bayes’ rule or Bayes’ law. One of the many applications of Bayes’s theorem is Bayesian inference which is one of the approaches of statistical inference (other being Frequentist inference), and fundamental to Bayesian statistics. In this post, you will learn about the following: Introduction to Bayes’ Theorem Bayes’ theorem real-world examples Introduction to Bayes’ Theorem In simple words, Bayes Theorem is used to determine the probability of a hypothesis in the presence of more evidence or information. In other …

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Joint & Conditional Probability Explained with Examples

In this post, you will learn about joint and conditional probability differences and examples. When starting with Bayesian analytics, it is very important to have a good understanding around probability concepts. And, the probability concepts such as joint and conditional probability is fundamental to probability and key to Bayesian modeling in machine learning. As a data scientist, you must get a good understanding of probability related concepts. Joint & Conditional Probability Concepts In this section, you will learn about basic concepts in relation to Joint and conditional probability. Probability of an event can be quantified as a function of uncertainty of whether that event will occur or not. Let’s say an event A is …

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