On the role of the overall effect in exponential families

@article{Klimova2017OnTR,
  title={On the role of the overall effect in exponential families},
  author={Anna Klimova and Tam{\'a}s Rudas},
  journal={arXiv: Methodology},
  year={2017}
}
Exponential families of discrete probability distributions when the normalizing constant (or overall effect) is added or removed are compared in this paper. The latter setup, in which the exponential family is curved, is particularly relevant when the sample space is an incomplete Cartesian product or when it is very large, so that the computational burden is significant. The lack or presence of the overall effect has a fundamental impact on the properties of the exponential family. When the… 
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References

SHOWING 1-10 OF 29 REFERENCES
Iterative Scaling in Curved Exponential Families
The paper describes a generalized iterative proportional fitting procedure that can be used for maximum likelihood estimation in a special class of the general log‐linear model. The models in this…
On the closure of relational models
Two algorithms for fitting constrained marginal models
Context-specific independence in graphical log-linear models
TLDR
It is illustrated that lifting the restriction of decomposability makes the models more expressive, such that additional context-specific independencies embedded in real data can be identified.
Estimation for multiplicative models under multinomial sampling
The models considered in this paper are a special subclass of Relational models which may be appropriate when a collection of independence statements must hold even after probabilities are re-scaled…
On the toric algebra of graphical models
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general…
Coordinate-free analysis of trends in British social mobility
This paper is intended to make a contribution to the ongoing debate about declining social mobility in Great Britain by analyzing mobility tables based on data from the 1991 British Household Panel…
Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, 3/e (Undergraduate Texts in Mathematics)
Algebraic Geometry is the study of systems of polynomial equations in one or more variables, asking such questions as: Does the The denominator is taking on this, book interested. This book for…
Probabilistic Graphical Models - Principles and Techniques
TLDR
The framework of probabilistic graphical models, presented in this book, provides a general approach for causal reasoning and decision making under uncertainty, allowing interpretable models to be constructed and then manipulated by reasoning algorithms.
When and why are log-linear models self-normalizing?
TLDR
A novel variant of an established procedure for training self-normalized models is described, which avoids computing normalizers for most training examples, and decreases training time by as much as factor of ten while preserving model quality.
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