Integrated Bayesian Inference Framework: Markov Chain Monte Carlo, Hyperdimensional Computing, Knowledge Graphs and GNNs

"Bayesian inference stands as a cornerstone in the realm of statistical modelling and machine learning, offering us a robust framework for updating beliefs in light of new evidence. This article dives into an intricate Python-based Bayesian inference framework I created that amalgamates several advanced computational techniques: Markov Chain Monte Carlo (MCMC), Hyperdimensional Computing (HDC), Knowledge Graphs and Graph Neural Networks (GNNs).

"I will explore the mathematical underpinnings of each component, dissect the accompanying code with rich snippets and deep explanations, examine practical use cases, highlight the benefits of this integrated approach and elucidate the results obtained from executing the framework."

Originally posted by Zscale Labs™ colleague Robert McMenemy. Continue reading at:

Integrated Bayesian Inference Framework: Markov Chain Monte Carlo, Hyperdimensional Computing, Knowledge Graphs and GNNs

#BayesianInference #MCMC #HyperdimensionalComputing #MachineLearning #AI #DataScience #ProbabilisticModeling #Statistics #ComputationalIntelligence #NeuralNetworks #CognitiveComputing #ComplexSystems #AdvancedAnalytics #BigData #PatternRecognition #DeepLearning #AlgorithmicIntelligence #PredictiveModeling #InformationTheory #ComputationalNeuroscience