Bayes Server

Artificial intelligence, Causal AI, machine learning and decision automation software based on Bayesian networks.

[Bayes Server @BayesServer]

Bayes Server Online now updated to 11.4. Try all the features of Bayes Server in the browser without installing any software https://t.co/TKXBTeZ01I

[Bayes Server @BayesServer]

Apple Mac desktop app has been reinstated, with new installer, signed and notarized for a smoother, more secure experience—no more warnings, just install and go. It’s universal (arm64 + x64), so it runs seamlessly on both Apple Silicon and Intel Macs. https://t.co/IqiYc2W0bL

[Bayes Server @BayesServer]

Bayes Server 11.4 is now available. Versions 11.4 and 11.3 include improvements to the distribution editor, bug fixes (including DBN issues in the UI). https://t.co/iSvDm0rKwL

[Bayes Server @BayesServer]

New video on how to automatically extract key insights from a Bayesian network, using the @bayesserver Auto Insight tool...

https://t.co/VZLy2WyDUk

[Bayes Server @BayesServer]

Scenarios explained...
https://t.co/wVP8r60nnb

[Bayes Server @BayesServer]

New video to accompany the tutorial Discrete network | manual construction...
https://t.co/f2vTNmcCbi

[Bayes Server @BayesServer]

New tutorial video on Data Sampling and updated Data Sampling help...
https://t.co/FZIkvYQTXS
https://t.co/h8bn64rvKp

[Bayes Server @BayesServer]

The Gartner® 2025 Hype Cycle for AI is out! Bayes Server has been recognized as a key vendor in Causal AI, now positioned at the Innovation Trigger stage of the cycle.

[Bayes Server @BayesServer]

Junction trees typically consume more memory than the original node distributions.
It can be important to understand these internal distributions, so you can analyze the performance of your network.

[Bayes Server @BayesServer]

Bayes Server 11. Visualize multiple scenarios. Internal calculation visuals. log-likelihood density estimation. info theory calculators
https://t.co/IqiYc2W0bL

[Bayes Server @BayesServer]

New d-separation (conditional independence) tutorial.
https://t.co/oxA5M8xG8e

We will use the Waste sample network, included with Bayes Server, to demonstrate how to use D-Separation to determine which variable(s) in a network are (conditionally) independent of each other.

[Bayes Server @BayesServer]

New Data Sampling tutorial. https://t.co/465s3MgNvS

Generate sample data, which is useful in many different applications. Include missing data if required. Also, include fixed data and understand how that effects the log-likelihood.

[Bayes Server @BayesServer]

New retracted analysis tutorial https://t.co/aRezvr9CGQ

The retracted-analysis tool helps us understand which variable(s) are driving unusual behavior for a data set. We could use it to analyze anomalies for example.

[Bayes Server @BayesServer]

New Log-Likelihood tutorial https://t.co/H0c9DjMKSZ
In this tutorial, we explore how to use the Log Likelihood query and Log-Likelihood batch query. What’s log-likelihood all about? It helps measure how unusual certain evidence is, making it a powerful tool for anomaly detection

[Bayes Server @BayesServer]

New Impact analysis tutorial.
https://t.co/kPVerWTx0D
Evaluate how sets of evidence influence a specific variable. Gauge the effect of particular data points on an outcome, providing valuable insights into which pieces of evidence are most significant in determining the result.

[Bayes Server @BayesServer]

New Pattern analysis tutorial:
https://t.co/PSAP3vfOB2

The Pattern Analysis tool helps you explore how one state of a target variable stands out compared to others, based on relationships in the network. Perfect for uncovering unique insights.

[Bayes Server @BayesServer]

New Auto-Insight tutorial
https://t.co/VZLy2WyDUk

The Auto Insight tool in Bayes Server helps you analyze differences in target variable states based on other variables in your network. Perfect for uncovering patterns and insights

[Bayes Server @BayesServer]

New Value of information tutorial:
https://t.co/1yPBWZfhlf
Does a system issue affect flight safety? Value of Information can help! Use a goal-focused approach, to find the next key variable, reducing uncertainty about potential failures Whether in healthcare or aviation.

[Bayes Server @BayesServer]

New article on Value of information https://t.co/Ia7Aq5uVRn

Bayesian networks can highlight which data reduces uncertainty most effectively. Higher Entropy indicates greater uncertainty, so new information with high impact can significantly increase decision-making confidence.

[Bayes Server @BayesServer]

New tutorials available:

Discrete Network | Manual Construction https://t.co/sKVJas3QDV

Parameter learning https://t.co/4mqtzrLAhD

Hybrid Network | Manual Construction Tutorial https://t.co/E5MYaFX2MO