Enes Makalic

According to our database, Enes Makalic authored at least 36 papers between 2003 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Maximum likelihood estimation of the Weibull distribution with reduced bias.

Enes Makalic, Daniel F. Schmidt

Stat. Comput., June, 2023

Minimum Message Length Inference of the Weibull Distribution with Complete and Censored Data.

Enes Makalic, Daniel F. Schmidt

Proceedings of the AI 2023: Advances in Artificial Intelligence, 2023

2022

An efficient algorithm for sampling from sin^<i>k</i> (<i>x</i>) for generating random correlation matrices.

Enes Makalic, Daniel F. Schmidt

Commun. Stat. Simul. Comput., 2022

Sparse Horseshoe Estimation via Expectation-Maximisation.

Shu Yu Tew, Daniel F. Schmidt, Enes Makalic

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2021

Minimum Message Length Inference of the Exponential Distribution with Type I Censoring.

Enes Makalic, Daniel Francis Schmidt

Entropy, 2021

2019

Bayesian Generalized Horseshoe Estimation of Generalized Linear Models.

Daniel F. Schmidt, Enes Makalic

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

2018

Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors.

Daniel F. Schmidt, Enes Makalic

CoRR, 2018

2017

Robust Lasso Regression with Student-t Residuals.

Daniel F. Schmidt, Enes Makalic

Proceedings of the AI 2017: Advances in Artificial Intelligence, 2017

2016

A Simple Sampler for the Horseshoe Estimator.

Enes Makalic, Daniel F. Schmidt

IEEE Signal Process. Lett., 2016

Bayesian Grouped Horseshoe Regression with Application to Additive Models.

Zemei Xu, Daniel F. Schmidt, Enes Makalic, Guoqi Q. Qian, John L. Hopper

Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

Approximating Message Lengths of Hierarchical Bayesian Models Using Posterior Sampling.

Daniel F. Schmidt, Enes Makalic, John L. Hopper

Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

Bayesian Robust Regression with the Horseshoe+ Estimator.

Enes Makalic, Daniel F. Schmidt, John L. Hopper

Proceedings of the AI 2016: Advances in Artificial Intelligence, 2016

2015

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.

Benjamin Goudey, Mani Abedini, John L. Hopper, Michael Inouye, Enes Makalic, Daniel F. Schmidt, John Wagner, Zeyu Zhou, Justin Zobel, Matthias Reumann

Health Inf. Sci. Syst., 2015

2013

Minimum Message Length Ridge Regression for Generalized Linear Models.

Daniel F. Schmidt, Enes Makalic

Proceedings of the AI 2013: Advances in Artificial Intelligence, 2013

DEPTH: A Novel Algorithm for Feature Ranking with Application to Genome-Wide Association Studies.

Enes Makalic, Daniel F. Schmidt, John L. Hopper

Proceedings of the AI 2013: Advances in Artificial Intelligence, 2013

2012

The Consistency of MDL for Linear Regression Models With Increasing Signal-to-Noise Ratio.

Daniel Francis Schmidt, Enes Makalic

IEEE Trans. Signal Process., 2012

Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results.

Matthias Reumann, Enes Makalic, Benjamin W. Goudey, Michael Inouye, Adrian Bickerstaffe, Minh Bui, Daniel J. Park, Miroslaw K. Kapuscinski, Daniel F. Schmidt, Zeyu Zhou, Guoqi Q. Qian, Justin Zobel, John Wagner, John L. Hopper

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions.

Daniel F. Schmidt, Enes Makalic

Proceedings of the AI 2012: Advances in Artificial Intelligence, 2012

MML Logistic Regression with Translation and Rotation Invariant Priors.

Enes Makalic, Daniel F. Schmidt

Proceedings of the AI 2012: Advances in Artificial Intelligence, 2012

2011

Estimating the Order of an Autoregressive Model Using Normalized Maximum Likelihood.

Daniel Francis Schmidt, Enes Makalic

IEEE Trans. Signal Process., 2011

Minimum Message Length Analysis of the Behrens-Fisher Problem.

Enes Makalic, Daniel F. Schmidt

Proceedings of the Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, 2011

MMLD Inference of Multilayer Perceptrons.

Enes Makalic, Lloyd Allison

Proceedings of the Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, 2011

A Simple Bayesian Algorithm for Feature Ranking in High Dimensional Regression Problems.

Enes Makalic, Daniel Francis Schmidt

Proceedings of the AI 2011: Advances in Artificial Intelligence, 2011

Logistic Regression with the Nonnegative Garrote.

Enes Makalic, Daniel Francis Schmidt

Proceedings of the AI 2011: Advances in Artificial Intelligence, 2011

2010

Fast Computation of the Kullback-Leibler Divergence and Exact Fisher Information for the First-Order Moving Average Model.

Enes Makalic, Daniel Francis Schmidt

IEEE Signal Process. Lett., 2010

The Behaviour of the Akaike Information Criterion When Applied to Non-nested Sequences of Models.

Daniel Francis Schmidt, Enes Makalic

Proceedings of the AI 2010: Advances in Artificial Intelligence, 2010

Review of Modern Logistic Regression Methods with Application to Small and Medium Sample Size Problems.

Enes Makalic, Daniel Francis Schmidt

Proceedings of the AI 2010: Advances in Artificial Intelligence, 2010

2009

Universal models for the exponential distribution.

Daniel Francis Schmidt, Enes Makalic

IEEE Trans. Inf. Theory, 2009

Towards the Interpretation of Utterance Sequences in a Dialogue System.

Ingrid Zukerman, Patrick Ye, Kapil Kumar Gupta, Enes Makalic

Proceedings of the SIGDIAL 2009 Conference, 2009

MML Invariant Linear Regression.

Daniel Francis Schmidt, Enes Makalic

Proceedings of the AI 2009: Advances in Artificial Intelligence, 2009

2008

A Probabilistic Approach to the Interpretation of Spoken Utterances.

Ingrid Zukerman, Enes Makalic, Michael Niemann, Sarah George

Proceedings of the PRICAI 2008: Trends in Artificial Intelligence, 2008

A Probabilistic Model for Understanding Composite Spoken Descriptions.

Enes Makalic, Ingrid Zukerman, Michael Niemann, Daniel Francis Schmidt

Proceedings of the PRICAI 2008: Trends in Artificial Intelligence, 2008

A spoken language interpretation component for a robot dialogue system.

Enes Makalic, Ingrid Zukerman, Michael Niemann

Proceedings of the INTERSPEECH 2008, 2008

Using Probabilistic Feature Matching to Understand Spoken Descriptions.

Ingrid Zukerman, Enes Makalic, Michael Niemann

Proceedings of the AI 2008: Advances in Artificial Intelligence, 2008

2007

Hypothesis Generation and Maintenance in the Interpretation of Spoken Utterances.

Michael Niemann, Ingrid Zukerman, Enes Makalic, Sarah George

Proceedings of the AI 2007: Advances in Artificial Intelligence, 2007

2003

MML Classification of Music Genres.

Adrian C. Bickerstaffe, Enes Makalic

Proceedings of the AI 2003: Advances in Artificial Intelligence, 2003