## 📄️ Introduction to anomaly detection

Discover how to build anomaly detection systems with Bayesian networks. Learn about supervised and unsupervised techniques, predictive maintenance and time series anomaly detection.

## 📄️ Prediction with Bayesian networks

Discover how to make complex predictions with Bayesian networks. Learn about marginal, joint & conditional probability queries, model verification, time series prediction, anomaly detection and most probable explanations.

## 📄️ Diagnostics, troubleshooting & reasoning | Bayesian networks

Discover how to perform diagnostics, troubleshooting & reasoning with Bayesian networks. Learn about forwards & backwards reasoning and analytical techniques such as Value of Information (VOI) and tracing of anomalies.

## 📄️ Introduction to decision automation | Bayes Server

Discover how to automate decisions with Bayesian networks. Learn what Decision graphs (Influence diagrams) are and how they can be used to make decisions under uncertainty.

## 📄️ Automated insight using Bayesian networks

Discover how to automatically extract significant insight from data using Bayesian networks. Learn how manual report inspection and charting can be replaced with algorithms that automate the process.

## 📄️ Latent variables in Bayesian networks

Discover how latent variables can be used to build sophisticated models capable of capturing complex hidden non linear relationships in data (automatic feature extraction).

## 📄️ Virtual / soft accuracy

Discover how virtual / soft accuracy can be used to build models which do not perform well overall, but do perform well under certain conditions.

## 📄️ Mixture models with Bayesian networks

Discover how to build a mixture model using Bayesian networks, and then how they can be extended to build more complex models.

## 📄️ Time series model types

Well known Time Series models as Dynamic Bayesian networks

## 📄️ Risk modeling with Bayesian networks

Getting started Risk modeling with Bayesian networks