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Statistical rigor in LiDAR-assisted estimation of aboveground forest biomass

Timothy G. Gregoire, Erik Næsset, Ronald E. McRoberts, Göran Ståhl, Hans-Erik Andersen, Terje Gobakken, Liviu Ene, Ross Nelson
2016 Remote Sensing of Environment  
In the rapidly expanding literature on LiDAR-assisted biomass estimation the assessment of the uncertainty of estimation varies widely, ranging from statistically rigorous to ad hoc.  ...  In the past decade, airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool for sample surveys aimed at improving estimation of aboveground forest biomass.  ...  Statistical inference, sampling, and sampling error One goal of survey sampling is to infer something about the population that has been sampled.  ... 
doi:10.1016/j.rse.2015.11.012 fatcat:nomllosn6zgi7n6hnwcj4om4ra

Decision Support in Health Care via Root Evidence Sampling

Benjamin Perry, Eli Faulkner
2007 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)  
We propose Root Evidence Sampling (RES), an algorithm that carefully reorganizes some or all of the evidence nodes to be root nodes, computes new conditional probability tables, and then uses simple forward  ...  Bayesian networks play a key role in decision support within health care. Physicians rely on Bayesian networks to give medical treatment, generate what-if scenarios, and other decision-support tasks.  ...  The probability of each sample is computed and stored with the sample. After enough samples have been generated, the posterior probability of each node and each state can be computed.  ... 
doi:10.1109/hicss.2007.163 dblp:conf/hicss/PerryF07 fatcat:aoixjekrzbchbavtgfc2yntgui

Page 5 of The American Statistician Vol. 15, Issue 5 [page]

1961 The American Statistician  
Intensive work will be given in the fields of control charts, acceptance sampling, and re- lated topics.  ...  This should permit relative evaluation of such techniques with respect to their probable future utili- zation in various aspects of the computing field.  ... 

Estimation for inaccessible and non-sampled forest areas using model-based inference and remotely sensed auxiliary information

Ronald E. McRoberts, Erik Næsset, Terje Gobakken
2014 Remote Sensing of Environment  
Model-based inference does not require probability samples and when used with synthetic estimation can circumvent small or no-sample difficulties associated with probability-based inference.  ...  The estimates were compared to simple random sampling, model-assisted, and model-based estimates that would have been obtained if the areas had been sampled.  ...  Acknowledgement The authors thank Mr. Brian F. Wilson of the Northern Research Station, U.S. Forest Service, for assistance with graphics.  ... 
doi:10.1016/j.rse.2014.08.028 fatcat:xu4xc6iiojc7zddii2b7vjtv6q

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

Göran Ståhl, Svetlana Saarela, Sebastian Schnell, Sören Holm, Johannes Breidenbach, Sean P. Healey, Paul L. Patterson, Steen Magnussen, Erik Næsset, Ronald E. McRoberts, Timothy G. Gregoire
2016 Forest Ecosystems  
Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies  ...  The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data.  ...  ., are likely to require probability field samples.  ... 
doi:10.1186/s40663-016-0064-9 fatcat:clfbkmytmzadzfsyquci5cdpoy

Active Intent Disambiguation for Shared Control Robots

Deepak E. Gopinath, Brenna D. Argall
2020 IEEE transactions on neural systems and rehabilitation engineering  
As our last contribution, we introduce a novel field-theoretic approach to intent inference inspired by dynamic field theory that works in tandem with the disambiguation scheme.  ...  for providing appropriate assistance confidently and accurately.  ...  Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the aforementioned institutions.  ... 
doi:10.1109/tnsre.2020.2987878 pmid:32305928 fatcat:cmgkbnor5rdnhlo6fhkmhuncki

Statistical Themes and Lessons for Data Mining [chapter]

2006 Studies in Computational Intelligence  
Data mining is on the interface of Computer Science and Statistics, utilizing advances in both disciplines to make progress in extracting information from large databases.  ...  This article highlights some statistical themes and lessons that are directly relevant to data mining and attempts to identify opportunities where close cooperation between the statistical and computational  ...  for honesty and to take advantages of model averaging; don't confuse conditioning with intervening; and finally, don't take the error probabilities of hypothesis tests to be the error probabilities of  ... 
doi:10.1007/978-3-540-34351-6_10 fatcat:y52vkwxtrjc7dlbgmshugbf44e

Tapas of Algebraic Statistics

Carlos Améndola, Marta Casanellas, Luis David García-Puente
2018 Notices of the American Mathematical Society  
Algebraic statistics is an interdisciplinary field that uses tools from computational algebra, algebraic geometry, and combinatorics to address problems in statistics and its applications.  ...  The R package algstat contains many computational algebraic statistics tools including the state of the art implementation of the Diaconis-Sturmfels sampling method.  ...  Luis David García Puente Luis David García Puente works in applied and computational algebraic geometry, algebraic statistics, and algebraic combinatorics.  ... 
doi:10.1090/noti1713 fatcat:3lq3cspfajcz7oysx5lojzqbea

"Physics of Data", an innovative master programme in Physics

Marco Zanetti
2019 Zenodo  
All this calls for a substantial change in the way future physicists are taught: statistics and probability, information theory, machine learning as well as scientific computing and hardware setups should  ...  Most of the challenges set by modern physics endeavours are related to the management, processing and analysis of massive amount of data.  ...  S imultaneous inference of µ and σ from a sample: general Complex systems as example of theoretical physics relying and exploiting data to elaborate models • Quantum information and computing• Several  ... 
doi:10.5281/zenodo.3598764 fatcat:uttbuy7qxnhl5fqn5qypmp5vxa

Page 237 of Mathematics Teacher Vol. 53, Issue 4 [page]

1960 Mathematics Teacher  
Probability and statistics (Each group: 6 weeks) Textbook: “Introductory Probability and Statistical Inference—An Experimental Course’ (Revised Preliminary Edition) by the Commission on Mathematics.  ...  Congruence in F(X) and the proof that F(X) mod an irreducible polynomial is a field, the concepts of prime fields, ex- tension fields and root fields, the con- struction of projective geometries from fields  ... 

An Introduction to Variational Inference [article]

Ankush Ganguly, Samuel W. F. Earp
2021 arXiv   pre-print
We introduce the Evidence Lower Bound to tractably compute the approximated probability density and we review the ideas behind mean-field variational inference.  ...  With this paper, we aim to explain the concept of VI and assist in future research with this approach.  ...  Acknowledgements We would like to thank our colleagues Sanjana Jain, Dhruba Pujary and, Ukrit Watchareeruetai for their constructive input and feedback during the writing of this paper.  ... 
arXiv:2108.13083v3 fatcat:2jldfx6dtzdijczxdbav7t7lge

Designing for different levels of social inference risk

Sara Motahari, Sotirios Ziavras, Quentin Jones
2009 Proceedings of the 5th Symposium on Usable Privacy and Security - SOUPS '09  
The opinions expressed are those of the authors and may not reflect those of the NSF.  ...  Acknowledgments-This research is partially supported by the National Science Foundation Grant NSF IIS DST 0534520 and NSF CNS 0454081.  ...  Additional information such as the number of courses, statistical distribution of the number of students in a class, and enrollment statistics were obtained from university admission statistics.  ... 
doi:10.1145/1572532.1572562 dblp:conf/soups/MotahariZJ09 fatcat:sr5zfjp7cjgr5hx7vfsb2ed7ua

Bayesian Models for Phylogenetic trees

Clarence Leung
2012 McGill Science Undergraduate Research Journal  
inferring genetic ancestry of different species is a current challenge in phylogenetics because of the immense raw biological data to be analyzed. computational techniques are necessary in order to parse  ...  Methods: in this study, we analyzed a class of algorithms known as Markov Chain Monte Carlo (MCMC) algorithms, which uses Bayesian statistics on a biological model, and simulates the most likely evolutionary  ...  future worK Phylogenetics remains a significant field in both the biological sciences and computing.  ... 
doi:10.26443/msurj.v7i1.100 fatcat:ck7qefdwjfhtbf4c67zzo22ubq

Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley [article]

Ani Adhikari, John DeNero, Michael I. Jordan
2021 arXiv   pre-print
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and working on real-world  ...  major and minor program in data science.  ...  to design and teach the classes, to graduate students who have played key roles as teaching assistants and mentors, to staff who have helped to build the infrastructure behind the curriculum, to the departments  ... 
arXiv:2102.09391v3 fatcat:violqvfmmjfxlopog7mrkwn6xu

Partial Likelihood Inference For Time Series Following Generalized Linear Models

Konstantinos Fokianos, Benjamin Kedem
2004 Journal of Time Series Analysis  
-Conference Travel Award: Foundation of Statistical Inference: Applications in the Medical and Social Sciences, in Industry, and the Interface with Computer Science, Jerusalem, Israel, 2000.  ...  -Conference on Foundation of Statistical Inference: Applications in the Medical and Social Sciences, in Industry, and the Interface with Computer Science, Jerusalem, December 2000 (participation only).  ... 
doi:10.1046/j.0143-9782.2003.00344.x fatcat:zm42uhi2xvco5fgf3dzblptxha
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