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ORCIDEnes Makalic
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2020 – today
- 2024
[c27]Enes Makalic
, Daniel F. Schmidt:
Bayesian Parametric Proportional Hazards Regression with the Fused Lasso. AI (2) 2024: 151-161- 2023
- [j9]Enes Makalic, Daniel F. Schmidt:
Maximum likelihood estimation of the Weibull distribution with reduced bias. Stat. Comput. 33(3): 69 (2023) - [c26]Enes Makalic, Daniel F. Schmidt:
Minimum Message Length Inference of the Weibull Distribution with Complete and Censored Data. AI (1) 2023: 291-303 - 2022
- [j8]Enes Makalic, Daniel F. Schmidt:
An efficient algorithm for sampling from sink (x) for generating random correlation matrices. Commun. Stat. Simul. Comput. 51(5): 2731-2735 (2022) - [c25]Shu Yu Tew, Daniel F. Schmidt, Enes Makalic:
Sparse Horseshoe Estimation via Expectation-Maximisation. ECML/PKDD (5) 2022: 123-139 - [i2]Shu Yu Tew, Daniel F. Schmidt, Enes Makalic:
Sparse Horseshoe Estimation via Expectation-Maximisation. CoRR abs/2211.03248 (2022) - 2021
- [j7]Enes Makalic, Daniel Francis Schmidt:
Minimum Message Length Inference of the Exponential Distribution with Type I Censoring. Entropy 23(11): 1439 (2021)
2010 – 2019
- 2019
- [c24]Daniel F. Schmidt, Enes Makalic:
Bayesian Generalized Horseshoe Estimation of Generalized Linear Models. ECML/PKDD (2) 2019: 598-613 - 2018
- [i1]Daniel F. Schmidt, Enes Makalic:
Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors. CoRR abs/1801.02321 (2018) - 2017
- [c23]Daniel F. Schmidt, Enes Makalic:
Robust Lasso Regression with Student-t Residuals. Australasian Conference on Artificial Intelligence 2017: 365-374 - 2016
- [j6]Enes Makalic, Daniel F. Schmidt:
A Simple Sampler for the Horseshoe Estimator. IEEE Signal Process. Lett. 23(1): 179-182 (2016) - [c22]Zemei Xu, Daniel F. Schmidt, Enes Makalic, Guoqi Q. Qian, John L. Hopper:
Bayesian Grouped Horseshoe Regression with Application to Additive Models. Australasian Conference on Artificial Intelligence 2016: 229-240 - [c21]Enes Makalic, Daniel F. Schmidt, John L. Hopper:
Bayesian Robust Regression with the Horseshoe+ Estimator. Australasian Conference on Artificial Intelligence 2016: 429-440 - [c20]Daniel F. Schmidt, Enes Makalic, John L. Hopper:
Approximating Message Lengths of Hierarchical Bayesian Models Using Posterior Sampling. Australasian Conference on Artificial Intelligence 2016: 482-494 - 2015
- [j5]Benjamin Goudey, Mani Abedini, John L. Hopper, Michael Inouye, Enes Makalic, Daniel F. Schmidt, John Wagner, Zeyu Zhou, Justin Zobel, Matthias Reumann:
High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies. Health Inf. Sci. Syst. 3(1) (2015) - 2013
- [c19]Enes Makalic, Daniel F. Schmidt, John L. Hopper:
DEPTH: A Novel Algorithm for Feature Ranking with Application to Genome-Wide Association Studies. Australasian Conference on Artificial Intelligence 2013: 80-85 - [c18]Daniel F. Schmidt, Enes Makalic:
Minimum Message Length Ridge Regression for Generalized Linear Models. Australasian Conference on Artificial Intelligence 2013: 408-420 - 2012
- [j4]Daniel Francis Schmidt, Enes Makalic:
The Consistency of MDL for Linear Regression Models With Increasing Signal-to-Noise Ratio. IEEE Trans. Signal Process. 60(3): 1508-1510 (2012) - [c17]Daniel F. Schmidt, Enes Makalic:
Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions. Australasian Conference on Artificial Intelligence 2012: 672-682 - [c16]Enes Makalic, Daniel F. Schmidt:
MML Logistic Regression with Translation and Rotation Invariant Priors. Australasian Conference on Artificial Intelligence 2012: 878-889 - [c15]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:
Supercomputing enabling exhaustive statistical analysis of genome wide association study data: Preliminary results. EMBC 2012: 1258-1261 - 2011
- [j3]Daniel Francis Schmidt, Enes Makalic:
Estimating the Order of an Autoregressive Model Using Normalized Maximum Likelihood. IEEE Trans. Signal Process. 59(2): 479-487 (2011) - [c14]Enes Makalic, Daniel Francis Schmidt:
Logistic Regression with the Nonnegative Garrote. Australasian Conference on Artificial Intelligence 2011: 82-91 - [c13]Enes Makalic, Daniel Francis Schmidt:
A Simple Bayesian Algorithm for Feature Ranking in High Dimensional Regression Problems. Australasian Conference on Artificial Intelligence 2011: 223-230 - [c12]Enes Makalic, Daniel F. Schmidt:
Minimum Message Length Analysis of the Behrens-Fisher Problem. Algorithmic Probability and Friends 2011: 250-260 - [c11]Enes Makalic, Lloyd Allison:
MMLD Inference of Multilayer Perceptrons. Algorithmic Probability and Friends 2011: 261-272 - 2010
- [j2]Enes Makalic, Daniel Francis Schmidt:
Fast Computation of the Kullback-Leibler Divergence and Exact Fisher Information for the First-Order Moving Average Model. IEEE Signal Process. Lett. 17(4): 391-393 (2010) - [c10]Enes Makalic, Daniel Francis Schmidt:
Review of Modern Logistic Regression Methods with Application to Small and Medium Sample Size Problems. Australasian Conference on Artificial Intelligence 2010: 213-222 - [c9]Daniel Francis Schmidt, Enes Makalic:
The Behaviour of the Akaike Information Criterion When Applied to Non-nested Sequences of Models. Australasian Conference on Artificial Intelligence 2010: 223-232
2000 – 2009
- 2009
- [j1]Daniel Francis Schmidt, Enes Makalic:
Universal models for the exponential distribution. IEEE Trans. Inf. Theory 55(7): 3087-3090 (2009) - [c8]Daniel Francis Schmidt, Enes Makalic:
MML Invariant Linear Regression. Australasian Conference on Artificial Intelligence 2009: 312-321 - [c7]Ingrid Zukerman, Patrick Ye, Kapil Kumar Gupta, Enes Makalic:
Towards the Interpretation of Utterance Sequences in a Dialogue System. SIGDIAL Conference 2009: 46-53 - 2008
- [c6]Ingrid Zukerman, Enes Makalic, Michael Niemann:
Using Probabilistic Feature Matching to Understand Spoken Descriptions. Australasian Conference on Artificial Intelligence 2008: 157-167 - [c5]Enes Makalic, Ingrid Zukerman, Michael Niemann:
A spoken language interpretation component for a robot dialogue system. INTERSPEECH 2008: 195-198 - [c4]Ingrid Zukerman, Enes Makalic, Michael Niemann, Sarah George:
A Probabilistic Approach to the Interpretation of Spoken Utterances. PRICAI 2008: 581-592 - [c3]Enes Makalic, Ingrid Zukerman, Michael Niemann, Daniel Francis Schmidt:
A Probabilistic Model for Understanding Composite Spoken Descriptions. PRICAI 2008: 750-759 - 2007
- [c2]Michael Niemann, Ingrid Zukerman, Enes Makalic, Sarah George:
Hypothesis Generation and Maintenance in the Interpretation of Spoken Utterances. Australian Conference on Artificial Intelligence 2007: 466-475 - 2003
- [c1]Adrian C. Bickerstaffe, Enes Makalic:
MML Classification of Music Genres. Australian Conference on Artificial Intelligence 2003: 1063-1071
Coauthor Index
aka: Daniel Francis Schmidt
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