P. Kontkanen

发表

Bayesian network structure learning using factorized NML universal models

T. Roos, T. Silander, P. Kontkanen, 2008, 2008 Information Theory and Applications Workshop.

EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCEAPPROXIMATION FOR PREDICTIVE MODELING AND DATA MININGPetri

P. Kontkanen, Petri Myllym, aki Henry Tirri, 1997 .

Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures

P. Kontkanen, H. Tirri, P. Myllymäki, 1996 .

Comparing Bayesian Model Class Selection Criteriaby Discrete Finite

P. Kontkanen, H. Tirri, 1996 .

ANALYZING THE STOCHASTIC COMPLEXITY VIA TREE POLYNOMIALS

P. Kontkanen, P. Myllymäki, Petri Myllymäki, 2005 .

1 An MDL Framework for Data Clustering

P. Kontkanen, 2022 .

Some Experimental Results with Nite Mixture Models

P. Kontkanen, H. Tirri, Petri Myllym, 1996 .

Information-Theoretically Optimal Histogram Density Estimation

Petri Kontkanen, P. Kontkanen, 2006 .

The 15th IEEE Symposium on Personal, Indoor and Mobile Radio Communications, Barcelona, Spain, 2004

Henry Tirri, Teemu Roos, Hannes Wettig, 2004 .

Novel computational tools in bakery process data analysis: a comparative study

Juho Rousu, Marjaana Suutarinen, Petri Kontkanen, 2003 .

Comparing Soft Computing Methods in Prediction of Manufacturing Data

Henry Tirri, Petri Myllymäki, Petri Kontkanen, 1998, IEA/AIE.

Prababilistic Instance-Based Learning

Henry Tirri, Petri Myllymäki, Petri Kontkanen, 1996, ICML.

An MDL Framework for Data Clustering

Jorma Rissanen, Henry Tirri, Wray L. Buntine, 2005 .

Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering

Petri Kontkanen, P. Kontkanen, 2009 .

MDL Histogram Density Estimation

Petri Myllymäki, Petri Kontkanen, P. Kontkanen, 2007, AISTATS.

A Bayesian Framework for Case-Based Reasoning

Henry Tirri, Petri Myllymäki, Petri Kontkanen, 1996, EWCBR.

On Sensitivity of the MAP Bayesian Network Structure to the Equivalent Sample Size Parameter

Tomi Silander, Petri Myllymäki, Petri Kontkanen, 2007, UAI.

On Supervised Selection of Bayesian Networks pdf

Henry Tirri, Tomi Silander, Petri Myllymäki, 1999, UAI.

Factorized normalized maximum likelihood criterion for learning Bayesian network structures

Tomi Silander, Teemu Roos, Petri Myllymäki, 2008 .

On predictive distributions and Bayesian networks

Henry Tirri, Tomi Silander, Petri Myllymäki, 2000, Stat. Comput..

EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCEAPPROXIMATION FOR PREDICTIVE MODELING AND DATA MININGPetri

P. Kontkanen, Petri Myllym, aki Henry Tirri, 1997 .

NML Computation Algorithms for Tree-Structured Multinomial Bayesian Networks

Hannes Wettig, Petri Myllymäki, Petri Kontkanen, 2008, EURASIP J. Bioinform. Syst. Biol..

Comparing Predictive Inference Methods for Discrete Domains

P. Kontkanen, P. Myllymaki, H. Tirri, 2021 .

Predictive Data Mining with Finite Mixtures

Henry Tirri, Petri Myllymäki, Petri Kontkanen, 1996, KDD.

BAYDA: Software for Bayesian Classification and Feature Selection

Henry Tirri, Tomi Silander, Petri Myllymäki, 1998, KDD.

A Fast Normalized Maximum Likelihood Algorithm for Multinomial Data

Petri Myllymäki, Petri Kontkanen, P. Kontkanen, 2005, IJCAI.

A linear-time algorithm for computing the multinomial stochastic complexity

Petri Myllymäki, Petri Kontkanen, P. Kontkanen, 2007, Inf. Process. Lett..

PMC2 EFFICIENT DATA MINING AND PROBABILISTIC INFERENCE WITH P-COURSE: A BAYESIAN METHOD WITH MULTILEVEL PRIORS FOR MEDICAL APPLICATIONS

Petri Myllymäki, Petri Kontkanen, Jussi Lahtinen, 2006 .

Unsupervised Bayesian visualization of high-dimensional data

Henry Tirri, Petri Myllymäki, Petri Kontkanen, 2000, KDD '00.

Probabilistic Methods for Location Estimation in Wireless Networks

Henry Tirri, Teemu Roos, Hannes Wettig, 2005 .

Supervised model-based visualization of high-dimensional data

Henry Tirri, Tomi Silander, Petri Myllymäki, 2000, Intell. Data Anal..

On the Accuracy of Stochastic Complexity Approximations

Henry Tirri, Tomi Silander, Petri Myllymäki, 1999 .

Exploring the robustness of Bayesian and information-theoretic methods for predictive inference

Henry Tirri, Tomi Silander, Petri Myllymäki, 1999, AISTATS.

Bayes Optimal Instance-Based Learning

Henry Tirri, Tomi Silander, Petri Myllymäki, 1998, ECML.

Topics in probabilistic location estimation in wireless networks

Henry Tirri, Teemu Roos, Hannes Wettig, 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).