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This work focuses on off-policy evaluation (OPE) with function approximation in infinite-horizon undiscounted Markov decision processes (MDPs). For MDPs that are ergodic and linear (i.e. where rewards and dynamics are linear in some known…

Machine Learning · Computer Science 2020-06-24 Nevena Lazic , Dong Yin , Mehrdad Farajtabar , Nir Levine , Dilan Gorur , Chris Harris , Dale Schuurmans

We express the maximum likelihood (ML) degrees of a family toric varieties in terms of Mobius invariants of matroids. The family of interest are those parametrized by monomial maps given by Lawrence lifts of totally unimodular matrices with…

Combinatorics · Mathematics 2025-12-03 Taylor Brysiewicz , Aida Maraj

Over the last decades, the family of $\alpha$-stale distributions has proven to be useful for modelling in telecommunication systems. Particularly, in the case of radar applications, finding a fast and accurate estimation for the amplitude…

Methodology · Statistics 2023-11-15 Mahdi Teimouri

Suppose that $\{\lambda_n\}_{n=1}^{\infty}$ is a sequence of distinct positive real numbers satisfying the conditions inf$\{\lambda_{n+1}-\lambda_n \}>0,$ and $\sum_{n=1}^{\infty}\lambda_n^{-1}<\infty.$ We prove that the exponential system…

Functional Analysis · Mathematics 2024-01-03 Elias Zikkos , Gajath Gunatillake

The normality assumption on data set is very restrictive approach for modelling. The generalized form of normal distribution, named as an exponential power (EP) distribution, and its scale mixture form have been considered extensively to…

Statistics Theory · Mathematics 2017-07-20 Mehmet Niyazi Cankaya , Olcay Arslan

Bogdan et al. established a new criterion to determine the existence of a maximum likelihood estimator in discrete exponential families. It uses the notion of the set of uniqueness, which allows to apply the problem to the Ising model from…

Statistics Theory · Mathematics 2025-11-27 Tomasz Skalski , Tomasz Stroiński

In this work, we revisit the estimation of the model parameters of a Weibull distribution based on iid observations, using the maximum likelihood estimation (MLE) method which does not yield closed expressions of the estimators. Among other…

Computation · Statistics 2025-01-22 Buu-Chau Truong , Peter Mphekgwana , Nabendu Pal

In this paper, we study the log-likelihood function and Maximum Likelihood Estimate (MLE) for the matrix normal model for both real and complex models. We describe the exact number of samples needed to achieve (almost surely) three…

Representation Theory · Mathematics 2020-07-21 Harm Derksen , Visu Makam

The usual stochastic order and the likelihood ratio order between probability distributions on the real line are reviewed in full generality. In addition, for the distribution of a random pair $(X,Y)$, it is shown that the conditional…

Statistics Theory · Mathematics 2023-03-08 Lutz Duembgen , Alexandre Moesching

Statistical inference may follow a frequentist approach or it may follow a Bayesian approach or it may use the minimum description length principle (MDL). Our goal is to identify situations in which these different approaches to statistical…

Statistics Theory · Mathematics 2018-05-08 Peter Harremoës

Exponential families are a particular class of statistical manifolds which are particularly important in statistical inference, and which appear very frequently in statistics. For example, the set of normal distributions, with mean {\mu}…

Differential Geometry · Mathematics 2015-06-04 Mathieu Molitor

Families of mixtures of multivariate power exponential (MPE) distributions have been previously introduced and shown to be competitive for cluster analysis in comparison to other elliptical mixtures including mixtures of Gaussian…

Computation · Statistics 2023-01-24 Utkarsh J. Dang , Michael P. B. Gallaugher , Ryan P. Browne , Paul D. McNicholas

Exponential family distributions are highly useful in machine learning since their calculation can be performed efficiently through natural parameters. The exponential family has recently been extended to the t-exponential family, which…

Machine Learning · Statistics 2017-05-30 Futoshi Futami , Issei Sato , Masashi Sugiyama

We are concerned with an harmonic analysis in Hilbert spaces $L^2(\mu)$, where $\mu$ is a probability measure on $\br^n$. The unifying question is the presence of families of orthogonal (complex) exponentials $e_\lambda(x) = \exp(2\pi i…

Functional Analysis · Mathematics 2009-05-14 Dorin Ervin Dutkay , Palle E. T. Jorgensen , Deguang Han

We consider the problem of estimating (diagonally dominant) M-matrices as precision matrices in Gaussian graphical models. These models exhibit intriguing properties, such as the existence of the maximum likelihood estimator with merely two…

Machine Learning · Statistics 2023-06-12 Jiaxi Ying , José Vinícius de M. Cardoso , Daniel P. Palomar

The L1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix, or alternatively the underlying graph structure of a Gaussian Markov…

Machine Learning · Computer Science 2013-06-14 Cho-Jui Hsieh , Matyas A. Sustik , Inderjit S. Dhillon , Pradeep Ravikumar

Diffusion models have emerged as powerful tools for solving inverse problems, yet prior work has primarily focused on observations with Gaussian measurement noise, restricting their use in real-world scenarios. This limitation persists due…

Machine Learning · Statistics 2025-02-11 Alessandro Micheli , Mélodie Monod , Samir Bhatt

We show that the maximal number of equal entries in a totally positive (resp. totally nonsingular) $n\textrm{-by-}n$ matrix is $\Theta(n^{4/3})$ (resp. $\Theta(n^{3/2}$)). Relationships with point-line incidences in the plane, Bruhat order…

Combinatorics · Mathematics 2013-09-18 Miriam Farber , Mitchell Faulk , Charles R. Johnson , Evan Marzion

Boltzmann machines (BMs) are a class of binary neural networks for which there have been numerous proposed methods of estimation. Recently, it has been shown that in the fully visible case of the BM, the method of maximum pseudolikelihood…

Computation · Statistics 2014-09-30 Hien D. Nguyen , Ian A. Wood

The stochastic motions of a diffusing particle contain information concerning the particle's interactions with binding partners and with its local environment. However, accurate determination of the underlying diffusive properties, beyond…

Biological Physics · Physics 2016-12-21 Peter K. Koo , Simon G. J. Mochrie