Glossary
Bayesian Machine Learning
Fundamentals
Adversarial Machine LearningAI AgentsAI and EducationAI and MedicineAI AssistantsAI HardwareAI Voice TransferApproximate Dynamic ProgrammingBackpropagationBayesian Machine LearningCurse of DimensionalityData LabelingDeep LearningDeep Reinforcement LearningDimensionality ReductionF1 Score in Machine LearningFeedforward Neural NetworkFine Tuning in Deep LearningGated Recurrent UnitGenerative AILarge Language Model (LLM)Machine LearningMultimodal LearningNatural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Understanding (NLU)Precision and RecallTransformersZero-shot Classification Models
Models
AlphaGo ZeroBERTChatGPTChess botsDall-EDiffusion ModelsDistilBERTGoogle's BardHidden Markov Models (HMMs)Inference EngineLarge Language Model (LLM)Llama 2LLM CollectionMidjourney (Image Generation)MistralOpenAI WhisperPerceptronProbabilistic Models in Machine LearningRoBERTaRule-Based AISpeech-to-text modelsText-to-Speech ModelsXLNet
Techniques
Activation FunctionsBatch Gradient DescentBeam Search AlgorithmBenchmarkingClassificationCURE AlgorithmExpectation MaximizationFlajolet-Martin AlgorithmGaussian ProcessesGenerative Adversarial Networks (GANs)Gradient Boosting Machines (GBMs)Gradient ClippingGradient ScalingGroundingHyperparametersk-ShinglesLatent Dirichlet Allocation (LDA)Mixture of ExpertsMultimodal AIOnline Gradient DescentOverfitting and UnderfittingPrompt ChainingPrompt EngineeringRetrieval-Augmented Generation (RAG)RLHFSentiment AnalysisSequence ModelingSemantic KernelTokenizationWinnow AlgorithmWord Embeddings
Last updated on February 6, 202413 min read
Bayesian Machine Learning

Bayesian Machine Learning (BML) represents a sophisticated paradigm in the field of artificial intelligence, one that marries the power of statistical inference with machine learning.

Have you ever wondered how machine learning systems can improve their predictions over time, seemingly getting smarter with each new piece of data? This is not just a trait of all machine learning models but is particularly pronounced in Bayesian Machine Learning (BML), which stands apart for its ability to incorporate prior knowledge and uncertainty into its learning process. This article takes you on a deep dive into the world of BML, unraveling its concepts and methodologies, and showcasing its unique advantages, especially in scenarios where data is scarce or noisy. 

Note that Bayesian Machine Learning goes hand-in-hand with the concept of Probabilistic Models. To discover more about Probabilistic Models in Machine Learning, click here.

What is Bayesian Machine Learning?

Bayesian Machine Learning (BML) represents a sophisticated paradigm in the field of artificial intelligence, one that marries the power of statistical inference with machine learning. Unlike traditional machine learning, which primarily focuses on predictions, BML introduces the concept of probability and inference, offering a framework where learning evolves with the accumulation of evidence.

The cornerstone of BML is the integration of prior knowledge with new data. This fusion leads to a more nuanced and continuously improving model. For instance, a BML system might have prior knowledge that a patient with certain symptoms has a high chance of having a flu. As new patient data comes in, it refines its understanding and predictions about flu diagnoses.

Distinguishing BML from its traditional counterparts is the emphasis on probability and inference. While traditional machine learning excels with abundant data, BML shines when the data is sparse, yet the model is dense with complexity. This is where Bayesian inference steps in as a critical tool, as explained in Wolfram's introduction to Bayesian Inference, providing a method for statistical analysis that is both rigorous and intuitive.

At its heart, BML relies on Bayes' Theorem to compute conditional probabilities — the likelihood of an event occurring, given prior occurrence of another event. This statistical backbone enables BML to make predictions that are not just educated guesses but probabilistically informed assertions. Resources like yoursay.plos.org and statswithr.github.io delve deeper into these concepts for those seeking a more thorough understanding.

Central to the Bayesian analysis are three components:

  • Prior: The initial belief before considering new data.

  • Likelihood: The probability of observing the new data under various hypotheses.

  • Posterior: The updated belief after considering the new data.