2009 - IEEE Claude E. Shannon Award
1993 - IEEE Richard W. Hamming Medal "For fundamental contributions to information theory, statistical inference, control theory, and the theory of complexity."
Jorma Rissanen mainly investigates Algorithm, Minimum description length, Discrete mathematics, Information theory and Data compression. Jorma Rissanen combines subjects such as Statistics and Artificial intelligence with his study of Algorithm. He has researched Minimum description length in several fields, including Structure, Orthonormal basis, Stochastic complexity, Stochastic optimization and Statistical model.
The study incorporates disciplines such as Interval tree, Fractal tree index, Combinatorics, Logarithm and Class in addition to Discrete mathematics. His research integrates issues of Tree, Markov model, Asymptotically optimal algorithm, Upper and lower bounds and Segment tree in his study of Information theory. His research on Estimation theory also deals with topics like
His scientific interests lie mostly in Algorithm, Minimum description length, Artificial intelligence, Applied mathematics and Mathematical optimization. His Algorithm research incorporates elements of Coding and Arithmetic. His Minimum description length research focuses on subjects like Statistical model, which are linked to Model selection.
His Artificial intelligence research incorporates themes from Machine learning, Statistical inference and Pattern recognition. His Applied mathematics research includes elements of Structure and Stochastic process, Statistics, Fisher information, Estimation theory. His Mathematical optimization study combines topics in areas such as Optimal estimation, Linear model and Stochastic complexity.
Jorma Rissanen focuses on Science and engineering, Mathematical optimization, Minimum description length, Library science and Optimal estimation. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Logarithm, Noise and Applied mathematics. His Minimum description length study contributes to a more complete understanding of Algorithm.
The study of Algorithm is intertwined with the study of Model selection in a number of ways. He works mostly in the field of Optimal estimation, limiting it down to concerns involving Estimator and, occasionally, Information theory, Mutual information, Estimation theory, Probability and statistics and Statistical hypothesis testing. While the research belongs to areas of Kolmogorov structure function, Jorma Rissanen spends his time largely on the problem of Parameter space, intersecting his research to questions surrounding Discrete mathematics, Structure and Statistical model.
Jorma Rissanen mostly deals with Minimum description length, Cluster analysis, Data compression, Robustness and Applied mathematics. His Minimum description length research is within the category of Algorithm. His Cluster analysis research integrates issues from Data mining, Noise reduction and Wavelet, Wavelet transform, Pattern recognition.
His work deals with themes such as Tree, Sequence, Theoretical computer science and Lossy compression, which intersect with Data compression. His Applied mathematics research includes elements of Function, Statistics, Fisher information and Minimax. As a member of one scientific family, he mostly works in the field of Statistics, focusing on Asymptotically optimal algorithm and, on occasion, Statistical model.
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Paper: Modeling by shortest data description
A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH
Annals of Statistics (1983)
Stochastic Complexity In Statistical Inquiry
Universal coding, information, prediction, and estimation
IEEE Transactions on Information Theory (1984)
Stochastic Complexity and Modeling
Annals of Statistics (1986)
The minimum description length principle in coding and modeling
A. Barron;J. Rissanen;Bin Yu.
IEEE Transactions on Information Theory (1998)
SLIQ: A Fast Scalable Classifier for Data Mining
Manish Mehta;Rakesh Agrawal;Jorma Rissanen.
extending database technology (1996)
Fisher information and stochastic complexity
IEEE Transactions on Information Theory (1996)
A universal data compression system
IEEE Transactions on Information Theory (1983)
Universal modeling and coding
J. Rissanen;G. Langdon.
IEEE Transactions on Information Theory (1981)
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