Towards a General Vision System based on Symbol-Relation Grammars and Bayesian Networks Elias Ruiz, Augusto Melendez, and L. Enrique Sucar Computer Science Department, National Institute of Astrophysics, Optics and Elect

Source URL: ccc.inaoep.mx

A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky1,2,3 , Florian Franzen1,2,3 , Giacomo Bassetto1,3 , Jakob H. Macke1,3 1 Max Planck Institute for Biological Cy

Source URL: www.mackelab.org

• Computational neuroscience
• Statistics
• Neuroscience
• Statistical theory
• Neural networks
• Bioinformatics
• Artificial neural network
• Bayesian network
• Neural coding
• Variational Bayesian methods
• Bayesian inference
• Hidden Markov model

PROBABILISTIC MODELING OF GENE REGULATORY NETWORKS FROM DATA Thesis submitted for the degree “Doctor of Philosophy”

Source URL: www.cs.huji.ac.il

• Statistics
• Networks
• Learning
• Systems science
• Statistical models
• Systems biology
• Machine learning
• Bayesian network
• Gene regulatory network
• Nir Friedman
• Gaussian process
• Nonlinear dimensionality reduction

A Short Introduction to Probabilistic Soft Logic Angelika Kimmig1,2 , Stephen H. Bach1 , Matthias Broecheler3 , Bert Huang1 , Lise Getoor1 1 University of Maryland, 2 KU Leuven, 3 Aurelius LLC

Source URL: stephenbach.net

• Logic
• Philosophy
• Abstraction
• Bayesian statistics
• Markov networks
• Reasoning
• Model theory
• Semantics
• Probabilistic soft logic
• Markov logic network
• Probabilistic logic
• Statistical relational learning

Collective Activity Detection using Hinge-loss Markov Random Fields Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry Davis University of Maryland College Park, MD 20742 {blondon,sameh,bach,bert,g

Source URL: psl.umiacs.umd.edu

• Artificial intelligence
• Probability
• Statistics
• Bayesian statistics
• Markov networks
• Graphical models
• Markov random field
• Probability theory
• Image segmentation
• Probabilistic soft logic
• Activity recognition
• Support vector machine

Journal of Artificial Intelligence Research–774 Submitted 03/10; publishedIntrusion Detection using Continuous Time Bayesian Networks Jing Xu

Source URL: rlair.cs.ucr.edu

• Statistics
• Estimation theory
• Control theory
• Mathematical analysis
• Markov models
• Graphical models
• Bayesian network
• Maximum likelihood estimation
• Expectationmaximization algorithm
• Markov chain
• Discretization
• State-space representation

Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes E. Busra Celikkaya University of California, Riverside CELIKKAE @ CS . UCR . EDU

Source URL: rlair.cs.ucr.edu

• Graphical models
• Statistics
• Applied mathematics
• Mathematics
• Estimation theory
• Bayesian network
• Networks
• Gibbs sampling
• Belief propagation
• Expectationmaximization algorithm
• Computation tree

LNAIContinuous Time Bayesian Networks for Host Level Network Intrusion Detection

Source URL: rlair.cs.ucr.edu

• Statistics
• Probability
• Statistical theory
• Markov models
• Statistical models
• Graphical models
• Estimation theory
• Bayesian network
• Networks
• Markov chain
• Normal distribution
• Expectationmaximization algorithm

Robot Learning with a Spatial, Temporal, and Causal And-Or Graph Caiming Xiong∗ , Nishant Shukla∗ , Wenlong Xiong, and Song-Chun Zhu Abstract— We propose a stochastic graph-based framework for a robot to understand

Source URL: shukla.io

• Academia
• Applied mathematics
• Philosophy
• Networks
• Statistical models
• Knowledge representation
• Graph theory
• Bayesian network
• Causality
• Artificial neural network
• Motion planning
• Graph rewriting

PARTICLE FILTERS FOR EFFICIENT METER TRACKING WITH DYNAMIC BAYESIAN NETWORKS Ajay Srinivasamurthy∗ Andre Holzapfel†

Source URL: www.rhythmos.org

• Statistics
• Probability
• Monte Carlo methods
• Computational statistics
• Estimation theory
• Markov models
• Robot control
• Particle filter
• Auxiliary particle filter
• Hidden Markov model
• Normal distribution
• Resampling