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Ignorability in Statistical and Probabilistic Inference
2005
The Journal of Artificial Intelligence Research
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When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the ...
Since the modeling and computational complexities entailed by maintaining this proper distinction are often prohibitive, one asks for conditions under which it can be safely ignored. ...
Acknowledgments The author would like to thank Ian Pratt for providing the initial motivation for investigating the basis of probabilistic inference by conditioning. ...
doi:10.1613/jair.1657
fatcat:yqet5v3e4jd2tn4dlp5yi27fxy
Frameworks for prior-free posterior probabilistic inference
2014
Wiley Interdisciplinary Reviews: Computational Statistics
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2014 pre-print arXiv:1407.8225v1 fatcat:kirwkjdqufam3acyt657d3ahoi
The development of statistical methods for valid and efficient probabilistic inference without prior distributions has a long history. ...
In contrast, the inferential model (IM) framework is genuinely prior-free and is shown to be a promising new method for generating both valid and efficient probabilistic inference. ...
National Science Foundation, grants DMS-1007678, DMS-1208833, and DMS-1208841. ...
doi:10.1002/wics.1329
fatcat:cawblaq5arfm3l3ne32mwsyjgm
Imprecise probabilistic query answering using measures of ignorance and degree of satisfaction
2012
Annals of Mathematics and Artificial Intelligence
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We first propose an approach to measuring the ignorance of a probabilistic logic program with respect to a query. ...
The measure of ignorance (w.r.t. a query) reflects how reliable a precise probability for the query can be and a high value of ignorance suggests that a single probability is not suitable for the query ...
Acknowledgement This work is funded by the EPSRC projects with reference numbers: EP/D070864/1 and EP/D074282/1. ...
doi:10.1007/s10472-012-9286-x
fatcat:a74uvt6zazewndipyqtcr4x4am
Revisiting Haavelmo's structural econometrics: bridging the gap between theory and data
2015
Journal of Economic Methodology
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Third, to make a case that by blending some of Haavelmo's key methodological ideas and insights into a refined/extended Fisher-Neyman-Pearson statistical approach to inference, gives rise to a more pertinent ...
framework in economics. ...
In section 5, it is argued that in addition to Haavelmo's probabilistic perspective, textbook econometrics has also ignored numerous methodological ideas and insights pertaining to modeling with observational ...
doi:10.1080/1350178x.2015.1035946
fatcat:ivsps6uoejfp3ppqttawd24cka
The imprecise Dirichlet model
2009
International Journal of Approximate Reasoning
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For a general presentation of statistical inference with IP theories, see Walley [4], and the special issue of the Journal of Statistical Planning and Inference dedicated to that topic [1] . ...
The set is wide enough to yield vacuous prior probabilistic statements for many events and expectations, and can consequently be seen as a model for prior near-ignorance. ...
Let me also thank him and the authors for their patience. Finally, I also thank the 14 anonymous referees who reviewed the papers in this issue. ...
doi:10.1016/j.ijar.2008.03.007
fatcat:rzq6pxbic5eytkhpusnwexvham
Page 113 of Computational Linguistics Vol. 30, Issue 1
[page]
2004
Computational Linguistics
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Another weakness is the lack of discussion of statistical inferences, which are often necessary to interpret the probabilistic models themselves (is it reasonable, for example, to expect readers who actually ...
And for those who believe that probability is really only a description (a quantification of our ignorance, if you will), this volume neatly sum- marizes ways in which probability may play a key role in ...
Book ReviewProbabilistic Linguistics Rens Bod, Jennifer Hay and Stefanie Jannedy (editors) (University of Amsterdam, University of Canterbury, and Lucent Technologies) Cambridge, MA: The MIT Press, 2003, xii+451 pp; hardbound, ISBN 0-262-02536-1, $85.00; paperbound ISBN 0-262-52338-8, $35.00
2004
Computational Linguistics
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Another weakness is the lack of discussion of statistical inferences, which are often necessary to interpret the probabilistic models themselves (is it reasonable, for example, to expect readers who actually ...
A specific countercriticism of the quantitative practices in natural language pro- cessing is that the kinds of reasoning employed are unnatural and in many cases ignore important observations about linguistics ...
doi:10.1162/coli.2004.30.1.112
fatcat:ffi3l5623zbxzbu5a4j4upstey
Probabilistic Programming Process Algebra
[chapter]
2014
Lecture Notes in Computer Science
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However, handling data and uncertainty in a statistically meaningful way is an open problem in formal modelling, severely hampering the usefulness of these elegant tools in many real world applications ...
We present results from a prototype implementation of the language, demonstrating its usefulness in performing inference in a non-trivial example. ...
The authors thank Luca Cardelli and Vashti Galpin for their comments and suggestions. ...
doi:10.1007/978-3-319-10696-0_21
fatcat:vkqzg5d5dzafpoon73oi2xd4ze
Discussion: Foundations of Statistical Inference, Revisited
2014
Statistical Science
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With the constraints of Birnbaum's theorem lifted, we revisit the foundations of statistical inference, focusing on some new foundational principles, the inferential model framework, and connections with ...
This is an invited contribution to the discussion on Professor Deborah Mayo's paper, "On the Birnbaum argument for the strong likelihood principle," to appear in Statistical Science. ...
ACKNOWLEDGMENTS Supported in part by NSF Grants DMS-10-07678, DMS-12-08833 and DMS-12-08841. ...
doi:10.1214/14-sts472
fatcat:pvtvvpzt6nf2fnym62qram7s2y
Inferring phylogenies: an instant classic
2004
Heredity
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For that reason, many biologists view him as the father of statistical phylogenetics. It was with this in mind that I finally got my hands on his longawaited book, Inferring Phylogenies. ...
No other book gives as clear and comprehensive a picture of the major problems, solved or otherwise, in phylogenetic inference. ...
doi:10.1038/sj.hdy.6800575
fatcat:lwwexndvanb73jngmeivl3skja
Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification
2010
Journal of Econometrics
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away the problem of model validation, and (b) ignores the relevant error probabilities. ...
The paper argues for a return to the original statistical model specification problem, as envisaged by Fisher (1922) , where the task is understood as one of selecting a statistical model in such a way ...
problem of statistical model validation, and (b) ignore the relevant error probabilities for the inferences reached. ...
doi:10.1016/j.jeconom.2010.01.011
fatcat:pmpkf7ypffcztpdcys77o6dh4e
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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Logical AI is concerned with formal languages to represent and reason with qualitative specifications; statistical AI is concerned with learning quantitative specifications from data. ...
systems that are explainable and interpretable. ...
., 1995; Reiter, 2001] , where one is interested in knowledge and ignorance, and how that changes over actions. ...
doi:10.24963/ijcai.2017/733
dblp:conf/ijcai/Belle17
fatcat:xrnwnlyhdzbkdhc23g76ysnaky
Page 4128 of Mathematical Reviews Vol. , Issue 82j
[page]
1982
Mathematical Reviews
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and modus tollens, which is problematic probabilistically, and again not like the classically similar inference modus tollens. ...
inference of "if A then B’ from B—as above—and from ‘not-A* —which transpires to be very different probabilistically); the inference of ‘if not-A then B° from ‘if not-B then A‘ and also from ‘A or B’; ...
Probabilistic Inference and Probabilistic Reasoning
1990
Philosophical Topics
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We show here, first, that probabilistic reasoning and probabilistic inference are dtst tnct: second, that probat» 1ist ic inference is what both tr~d1tlon~11m1~JGt1ve log1c ("amp11at1ve Inference") t~nd ...
According to the other, uncertain inference is like deductive inference in that the conclusion is detached from the premises (the evidence) and accepted as "practically certain;" it differs in being non-monotonic ...
Probabilisitic Inference and Probabilistic Reasoning Uncertainty enters into human reasoning and inference in at least two ways. ...
doi:10.5840/philtopics19901826
fatcat:ucqonut4rjeabcnl5aoynyb5we
Is profile likelihood a true likelihood? An argument in favor
[article]
2018
arXiv
pre-print
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The connections to Bayesian inference can also be further clarified with the introduction of a suitable logarithmic distance function, in which case the present theory can be naturally described as 'Tropical ...
Bayes' in the sense of tropical algebra. ...
ACKNOWLEDGEMENTS The author would like to thank Michael Evans, Marco Cattaneo, Yudi Pawitan, Alexandre Patriota, Christian Robert and Anthony Edwards for useful comments and/or discussions. ...
arXiv:1801.04369v4
fatcat:utmkxy5u4rab7ech2szmpniwfa
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