Zobrazeno 1 - 10
of 616
pro vyhledávání: '"Chain rule (probability)"'
Autor:
Stephen D. Boul, Allison F. Toney
Publikováno v:
PRIMUS. 32:917-926
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2801f6cd3f06bf7f93d760912a1b8341
https://doi.org/10.1080/10511970.2021.1975180
https://doi.org/10.1080/10511970.2021.1975180
Autor:
Raymond T. Boute
Publikováno v:
SIAM REVIEW
The multivariable chain rule is often challenging to students because it is usually presented with ambiguities and other defects that hamper systematic and reliable application. A very simple formulation combines the derivation operators for function
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25279badf516cba12f2f04b1098e1bd7
https://doi.org/10.1137/19m1296732
https://doi.org/10.1137/19m1296732
Publikováno v:
AAAI
We consider the task of learning a parametric Continuous Time Markov Chain (CTMC) sequence model without examples of sequences, where the training data consists entirely of aggregate steady-state statistics. Making the problem harder, we assume that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d1d8f6649a5b31d5f3ffdd70942193c
https://doi.org/10.1609/aaai.v34i04.5806
https://doi.org/10.1609/aaai.v34i04.5806
The paper presents a novel approach by using multi- step predictions to address the adaptive sampling problem in a resources and obstacles constrained mobile robotic sensor network to efficiently monitor environmental spatial phenomena. It is first p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::283a3064370afc89ca4072c5797dd1e1
https://doi.org/10.36227/techrxiv.14642577.v2
https://doi.org/10.36227/techrxiv.14642577.v2
Publikováno v:
IJCNN
This paper proposes a compressed hidden naive Bayesian (C-HNB) classifier which is an improved version of hidden naive Bayesian (HNB) by compressing the Bayesian network structure and calculating the attribute correlation with maximal information coe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f451813b758a18a1ac90e18c8f07857
https://doi.org/10.1109/ijcnn52387.2021.9534201
https://doi.org/10.1109/ijcnn52387.2021.9534201
Autor:
Liesbeth Bruckers, Geert Molenberghs, Marc Aerts, Edmund Njeru Njagi, Geert Willem H. Schurink, Tarylee Reddy
Publikováno v:
Pharmaceutical Statistics, 18(6), 671-687. Wiley
Biomarkers play a key role in the monitoring of disease progression. The time taken for an individual to reach a biomarker exceeding or lower than a meaningful threshold is often of interest. Due to the inherent variability of biomarkers, persistence
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76d9dd25be78905781afc7a41cf1550b
https://doi.org/10.1002/pst.1956
https://doi.org/10.1002/pst.1956
Publikováno v:
Computational Statistics & Data Analysis. 137:67-91
A novel approach for parameter estimation in Bayesian networks is presented. The main idea is to introduce a hyper-prior in the Multinomial–Dirichletmodel, traditionally used for conditional distribution estimation in Bayesian networks. The resulti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1f8ea5386f55e6637f85abf0ffafcfa
https://doi.org/10.1016/j.csda.2019.02.004
https://doi.org/10.1016/j.csda.2019.02.004
Autor:
Fatih Kamisli, Aziz Berkay Yesilyurt
Publikováno v:
EUSIPCO
The use of neural networks in image compression enables transforms and probability models for entropy coding which can process images based on much more complex models than the simple Gauss-Markov models in traditional compression methods. All at the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49e460259dba558a1097fa8da2721e47
https://doi.org/10.23919/eusipco47968.2020.9287779
https://doi.org/10.23919/eusipco47968.2020.9287779
Autor:
Juan Pablo Vigneaux
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030802080
GSI
GSI
We consider the differential entropy of probability measures absolutely continuous with respect to a given \(\sigma \)-finite “reference” measure on an arbitrary measure space. We state the asymptotic equipartition property in this general case;
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dd9145a8c67bd05d80e76f3e441febdb
https://doi.org/10.1007/978-3-030-80209-7_38
https://doi.org/10.1007/978-3-030-80209-7_38
Autor:
Pankaj Prasad Dwivedi, D. K. Sharma
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030882433
Choosing the stochastic process with the entropy is attributable to increase the least amount of information to the problem under consideration. As a result, the entropy rate for stochastic processes must be determined. In the current correspondence,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8e5e2d4d0093885c2164ed6f312f5a55
https://doi.org/10.1007/978-3-030-88244-0_26
https://doi.org/10.1007/978-3-030-88244-0_26