Bayes

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• Feature Engineering and Selection: A Practical Approach for Predictive Models - 12.1 Naive Bayes Models | Max Kuhn and Kjell Johnson

This algorithm is based on the “Bayes’ Theorem” in probability. Due to that Naive Bayes can be applied only if the features are independent of each other since it is a requirement in Bayes’ Theorem. If we try to predict a flower type by its petal length and width, we can use Naive Bayes approach since both those features are independent. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium

Bayes' Theorem

the probability of an event, based on prior knowledge of conditions that might be related to the event. Bayes' Theorem | Wikipedia

Naive Bayes

• How to Develop a Naive Bayes Classifier from Scratch in Python | Jason Brownlee - Machine Learning Mastery
• A Beginner's Guide to Bayes' Theorem, Naive Bayes Classifiers and Bayesian Networks | Chris Nicholson - A.I. Wiki pathmind

A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.

Two-Class Bayes Point Machine

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This algorithm efficiently approximates the theoretically optimal Bayesian average of linear classifiers (in terms of generalization performance) by choosing one "average" classifier, the Bayes Point. Because the Bayes Point Machine is a Bayesian classification model, it is not prone to overfitting to the training data. - Microsoft

• Two-Class Bayes Point Machine | Microsoft

Bayesian Linear Regression

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• Regression Analysis
• Bayesian Linear Regression | Microsoft
• 10 types of regressions. Which one to use? | Vincent Granville

The aim of Bayesian Linear Regression is not to find the single “best” value of the model parameters, but rather to determine the posterior distribution for the model parameters. Not only is the response generated from a probability distribution, but the model parameters are assumed to come from a distribution as well. Introduction to Bayesian Linear Regression | Towards Data Science

In the Bayesian viewpoint, we formulate linear regression using probability distributions rather than point estimates. The response, y, is not estimated as a single value, but is assumed to be drawn from a probability distribution. The output, y is generated from a normal (Gaussian) Distribution characterized by a mean and variance. The mean for linear regression is the transpose of the weight matrix multiplied by the predictor matrix. The variance is the square of the standard deviation σ (multiplied by the Identity matrix because this is a multi-dimensional formulation of the model).

Bayesian methods have a highly desirable quality: they avoid overfitting. They do this by making some assumptions beforehand about the likely distribution of the answer. Another byproduct of this approach is that they have very few parameters. Machine Learning has both Bayesian algorithms for both classification (Two-class Bayes' point machine) and regression (Bayesian linear regression). Note that these assume that the data can be split or fit with a straight line. - Dinesh Chandrasekar

Bayesian Deep Learning (BDL)

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• Introduction to Bayesian Deep Learning | Daniel Gutierrez - Open Data Science (ODSC)

BDL provides a deep learning framework which can also model uncertainty. BDL can achieve state-of-the-art results, while also understanding uncertainty. Deep Learning Is Not Good Enough, We Need Bayesian Deep Learning for Safe AI | Alex Kendall

Bayes Estimator

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In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior expectation of a utility function. An alternative way of formulating an estimator within Bayesian statistics is maximum a posteriori estimation. Bayes Estimator | Wikipedia

Bayesian Network

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A Bayesian network (also known as a Bayes network, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms can perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (e.g. speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Bayesian Network | Wikipedia

Ensemble Learning

Bayes Optimal Classifier (BOC)

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• • Bayes Optimal Classifier (BOC) | Wikipedia

Bayesian Parameter Averaging (BPA)

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• • Bayesian Parameter Averaging (BPA) | Wikipedia

Bayesian Model Combination (BMC)

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• • Bayesian Model Combination (BMC) | Wikipedia