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Ignorability in Statistical and Probabilistic Inference

M. Jaeger
2005 The Journal of Artificial Intelligence Research  
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

Chuanhai Liu, Ryan Martin
2014 Wiley Interdisciplinary Reviews: Computational Statistics  
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

Anbu Yue, Weiru Liu, Anthony Hunter
2012 Annals of Mathematics and Artificial Intelligence  
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

Aris Spanos
2015 Journal of Economic Methodology  
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

Jean-Marc Bernard
2009 International Journal of Approximate Reasoning  
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  
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

Patrick Juola
2004 Computational Linguistics  
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]

Anastasis Georgoulas, Jane Hillston, Dimitrios Milios, Guido Sanguinetti
2014 Lecture Notes in Computer Science  
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

Ryan Martin, Chuanhai Liu
2014 Statistical Science  
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

A J Drummond
2004 Heredity  
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

Aris Spanos
2010 Journal of Econometrics  
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

Vaishak Belle
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
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  
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

Henry E. Kyburg,
1990 Philosophical Topics  
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]

Oliver J. Maclaren
2018 arXiv   pre-print
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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