# Paper presented at EUSIPCO 2015

Mikkel N. Schmidt presented the paper “Numerical approximations for speeding up MCMC inference in the infinite relational model” at EUSIPCO 2015.

# Article presented at MLSP 2015

Rasmus Røge presented the poster “Unsupervised Segmentation of Task Activated Regions in fMRI” at the Machine Learning for Signal Processing 2015 in Boston.

# Talk at the ICML workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015)

Morten Mørup gave a talk at ICML workshop on Statistics, Machine Learning and Neuroscience (Stamlins 2015) on Non-parametric Bayesian Modeling of Functional and Structural Brain Connectivity.

# Poster presented at HBM2014 on “Quantifying Temporal States in rs-fMRI Data using Bayesian Nonparametrics”

The poster on “Quantifying Temporal States in rs-fMRI Data using Bayesian Nonparametrics” was presented at HBM2014. To see the poster click here.

# Accepted NeuroImage article on modeling resting state fMRI networks using Non-parametric Bayesian relational models available online.

The NeuroImage article “Non-parametric Bayesian graph models reveal community structure in resting state fMRI” is available from:

http://www.sciencedirect.com/science/article/pii/S1053811914004686.

# Paper presented at Cognitive Information Processing (CIP2014) on modeling hierarchical structure in fMRI data

The paper “Discovering Hierarchical Structure In Normal Relational Data” was presented at Cognitive Information Processing (CIP2014), http://cip2014.conwiz.dk/home.htm#.U6AP0_l_t3E

# Paper accepted at PRNI 2014

Paper accepted at PRNI 2014 on modeling structural connectivity in full image resolution.

# Two abstracts accepted for OHBM 2014

The following two abstracts will be presented at OHBM 2014:

Josefine Korzen, Kristoffer Hougaard Madsen, Morten Mørup, *Quantifying Temporal States in rsfMRI Data using Bayesian Nonparametrics.*

Kasper Winther Andersen, Kristoffer H. Madsen, Hartwig Roman Siebner, Mikkel N. Schmidt, Morten Mørup, Lars Kai Hansen, *Community structure in resting state complex networks.*

# Paper accepted for MLSP2013

Paper accepted for the IEEE International Workshop on Machine Learning for Signal Processing 2013 on a high performance Gibbs sampler for relational modeling of complex networks.