Artigos de revistas sobre o tema "Exact and approximate inferences"
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Wu, Lang. "Exact and Approximate Inferences for Nonlinear Mixed-Effects Models With Missing Covariates". Journal of the American Statistical Association 99, n.º 467 (setembro de 2004): 700–709. http://dx.doi.org/10.1198/016214504000001006.
Texto completo da fonteMekhnacha, Kamel, Juan-Manuel Ahuactzin, Pierre Bessière, Emmanuel Mazer e Linda Smail. "Exact and approximate inference in ProBT". Revue d'intelligence artificielle 21, n.º 3 (12 de junho de 2007): 295–332. http://dx.doi.org/10.3166/ria.21.295-332.
Texto completo da fonteAkagi, Yasunori, Takuya Nishimura, Yusuke Tanaka, Takeshi Kurashima e Hiroyuki Toda. "Exact and Efficient Inference for Collective Flow Diffusion Model via Minimum Convex Cost Flow Algorithm". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 3163–70. http://dx.doi.org/10.1609/aaai.v34i04.5713.
Texto completo da fonteAbe, Takayuki, e Manabu Iwasaki. "EXACT AND APPROXIMATE INFERENCES FOR AN EXPONENTIAL MEAN FROM TYPE I CENSORED DATA". Bulletin of informatics and cybernetics 37 (dezembro de 2005): 31–39. http://dx.doi.org/10.5109/12589.
Texto completo da fonteYANG, HANN-PYI JAMES, e WEI-KEI SHIUE. "COMPARISON OF FAILURE INTENSITIES FROM TWO POISSON PROCESSES". International Journal of Reliability, Quality and Safety Engineering 02, n.º 03 (setembro de 1995): 235–43. http://dx.doi.org/10.1142/s0218539395000186.
Texto completo da fonteKarami, Md Jamil Hasan. "Assessing Goodness of Approximate Distributions for Inferences about Parameters in Nonlinear Regression Model". Dhaka University Journal of Science 71, n.º 1 (29 de maio de 2023): 13–16. http://dx.doi.org/10.3329/dujs.v71i1.65267.
Texto completo da fonteEl-Sagheer, Rashad M., Taghreed M. Jawa e Neveen Sayed-Ahmed. "Inferences for Generalized Pareto Distribution Based on Progressive First-Failure Censoring Scheme". Complexity 2021 (7 de dezembro de 2021): 1–11. http://dx.doi.org/10.1155/2021/9325928.
Texto completo da fonteLintusaari, Jarno, Paul Blomstedt, Tuomas Sivula, Michael U. Gutmann, Samuel Kaski e Jukka Corander. "Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth-death models". Wellcome Open Research 4 (25 de janeiro de 2019): 14. http://dx.doi.org/10.12688/wellcomeopenres.15048.1.
Texto completo da fonteLintusaari, Jarno, Paul Blomstedt, Brittany Rose, Tuomas Sivula, Michael U. Gutmann, Samuel Kaski e Jukka Corander. "Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models". Wellcome Open Research 4 (30 de agosto de 2019): 14. http://dx.doi.org/10.12688/wellcomeopenres.15048.2.
Texto completo da fonteShapovalova, Yuliya. "“Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study". Entropy 23, n.º 4 (15 de abril de 2021): 466. http://dx.doi.org/10.3390/e23040466.
Texto completo da fonteFioretto, Ferdinando, Enrico Pontelli, William Yeoh e Rina Dechter. "Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs". Constraints 23, n.º 1 (18 de agosto de 2017): 1–43. http://dx.doi.org/10.1007/s10601-017-9274-1.
Texto completo da fonteTarvirdizade, Bahman, e Hossein Kazemzadeh Garehchobogh. "Interval Estimation of Stress-Strength Reliability Based on Lower Record Values from Inverse Rayleigh Distribution". Journal of Quality and Reliability Engineering 2014 (16 de novembro de 2014): 1–8. http://dx.doi.org/10.1155/2014/192072.
Texto completo da fonteGuo, Yuanzhen, Hao Xiong e Nicholas Ruozzi. "Marginal Inference in Continuous Markov Random Fields Using Mixtures". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 7834–41. http://dx.doi.org/10.1609/aaai.v33i01.33017834.
Texto completo da fonteKenig, Batya, e Benny Kimelfeld. "Approximate Inference of Outcomes in Probabilistic Elections". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 2061–68. http://dx.doi.org/10.1609/aaai.v33i01.33012061.
Texto completo da fonteSeridi, Hamid, Herman Akdag, Rachid Mansouri e Mohamed Nemissi. "Approximate Reasoning in Supervised Classification Systems". Journal of Advanced Computational Intelligence and Intelligent Informatics 10, n.º 4 (20 de julho de 2006): 586–93. http://dx.doi.org/10.20965/jaciii.2006.p0586.
Texto completo da fonteTucci, Beatriz, e Fabian Schmidt. "EFTofLSS meets simulation-based inference: σ 8 from biased tracers". Journal of Cosmology and Astroparticle Physics 2024, n.º 05 (1 de maio de 2024): 063. http://dx.doi.org/10.1088/1475-7516/2024/05/063.
Texto completo da fonteDomínguez, E., e H. J. Kappen. "Efficient inference in the transverse field Ising model". Journal of Statistical Mechanics: Theory and Experiment 2023, n.º 3 (1 de março de 2023): 033301. http://dx.doi.org/10.1088/1742-5468/acba02.
Texto completo da fonteAtkinson, Eric, Charles Yuan, Guillaume Baudart, Louis Mandel e Michael Carbin. "Semi-symbolic inference for efficient streaming probabilistic programming". Proceedings of the ACM on Programming Languages 6, OOPSLA2 (31 de outubro de 2022): 1668–96. http://dx.doi.org/10.1145/3563347.
Texto completo da fonteDemidenko, Eugene. "Exact and Approximate Statistical Inference for Nonlinear Regression and the Estimating Equation Approach". Scandinavian Journal of Statistics 44, n.º 3 (29 de março de 2017): 636–65. http://dx.doi.org/10.1111/sjos.12269.
Texto completo da fonteÇakmak, Burak, Yue M. Lu e Manfred Opper. "Analysis of random sequential message passing algorithms for approximate inference". Journal of Statistical Mechanics: Theory and Experiment 2022, n.º 7 (1 de julho de 2022): 073401. http://dx.doi.org/10.1088/1742-5468/ac764a.
Texto completo da fonteRandone, Francesca, Luca Bortolussi, Emilio Incerto e Mirco Tribastone. "Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures". Proceedings of the ACM on Programming Languages 8, POPL (5 de janeiro de 2024): 1882–912. http://dx.doi.org/10.1145/3632905.
Texto completo da fonteDe Santis, Fulvio, e Stefania Gubbiotti. "Sample Size Requirements for Calibrated Approximate Credible Intervals for Proportions in Clinical Trials". International Journal of Environmental Research and Public Health 18, n.º 2 (12 de janeiro de 2021): 595. http://dx.doi.org/10.3390/ijerph18020595.
Texto completo da fonteCANO, ANDRÉS, MANUEL GÓMEZ-OLMEDO, CORA B. PÉREZ-ARIZA e ANTONIO SALMERÓN. "FAST FACTORISATION OF PROBABILISTIC POTENTIALS AND ITS APPLICATION TO APPROXIMATE INFERENCE IN BAYESIAN NETWORKS". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, n.º 02 (abril de 2012): 223–43. http://dx.doi.org/10.1142/s0218488512500110.
Texto completo da fonteSchälte, Yannik, e Jan Hasenauer. "Efficient exact inference for dynamical systems with noisy measurements using sequential approximate Bayesian computation". Bioinformatics 36, Supplement_1 (1 de julho de 2020): i551—i559. http://dx.doi.org/10.1093/bioinformatics/btaa397.
Texto completo da fonteDemidenko, Eugene, Benjamin B. Williams, Ann Barry Flood e Harold M. Swartz. "Standard error of inverse prediction for dose-response relationship: approximate and exact statistical inference". Statistics in Medicine 32, n.º 12 (5 de novembro de 2012): 2048–61. http://dx.doi.org/10.1002/sim.5668.
Texto completo da fonteVan den Broek, B., W. Wiegerinck e B. Kappen. "Graphical Model Inference in Optimal Control of Stochastic Multi-Agent Systems". Journal of Artificial Intelligence Research 32 (16 de maio de 2008): 95–122. http://dx.doi.org/10.1613/jair.2473.
Texto completo da fonteDaly, Aidan C., Jonathan Cooper, David J. Gavaghan e Chris Holmes. "Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models". Journal of The Royal Society Interface 14, n.º 134 (setembro de 2017): 20170340. http://dx.doi.org/10.1098/rsif.2017.0340.
Texto completo da fonteCabañas, Rafael, Manuel Gómez-Olmedo e Andrés Cano. "Using Binary Trees for the Evaluation of Influence Diagrams". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, n.º 01 (fevereiro de 2016): 59–89. http://dx.doi.org/10.1142/s0218488516500045.
Texto completo da fonteNAMPALLY, ARUN, TIMOTHY ZHANG e C. R. RAMAKRISHNAN. "Constraint-Based Inference in Probabilistic Logic Programs". Theory and Practice of Logic Programming 18, n.º 3-4 (julho de 2018): 638–55. http://dx.doi.org/10.1017/s1471068418000273.
Texto completo da fonteEnsinger, Katharina, Nicholas Tagliapietra, Sebastian Ziesche e Sebastian Trimpe. "Exact Inference for Continuous-Time Gaussian Process Dynamics". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 11 (24 de março de 2024): 11883–91. http://dx.doi.org/10.1609/aaai.v38i11.29074.
Texto completo da fonteCano, Andrés, Manuel Gómez, Serafín Moral e Joaquín Abellán. "Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks". International Journal of Approximate Reasoning 44, n.º 3 (março de 2007): 261–80. http://dx.doi.org/10.1016/j.ijar.2006.07.020.
Texto completo da fonteDrovandi, Christopher C., Anthony N. Pettitt e Roy A. McCutchan. "Exact and Approximate Bayesian Inference for Low Integer-Valued Time Series Models with Intractable Likelihoods". Bayesian Analysis 11, n.º 2 (junho de 2016): 325–52. http://dx.doi.org/10.1214/15-ba950.
Texto completo da fonteFeldman, A., G. Provan e A. Van Gemund. "Approximate Model-Based Diagnosis Using Greedy Stochastic Search". Journal of Artificial Intelligence Research 38 (27 de julho de 2010): 371–413. http://dx.doi.org/10.1613/jair.3025.
Texto completo da fonteTaghipour, N., D. Fierens, J. Davis e H. Blockeel. "Lifted Variable Elimination: Decoupling the Operators from the Constraint Language". Journal of Artificial Intelligence Research 47 (8 de julho de 2013): 393–439. http://dx.doi.org/10.1613/jair.3793.
Texto completo da fontevan Lieshout, M. N. M., e E. W. van Zwet. "Exact sampling from conditional Boolean models with applications to maximum likelihood inference". Advances in Applied Probability 33, n.º 2 (junho de 2001): 339–53. http://dx.doi.org/10.1017/s000186780001082x.
Texto completo da fonteAlnosaier, Waseem. "Comparisons of the Satterthwaite Approaches for Fixed Effects in Linear Mixed Models". International Journal of Statistics and Probability 13, n.º 1 (28 de fevereiro de 2024): 22. http://dx.doi.org/10.5539/ijsp.v13n1p22.
Texto completo da fonteMasegosa, Andrés R., Rafael Cabañas, Helge Langseth, Thomas D. Nielsen e Antonio Salmerón. "Probabilistic Models with Deep Neural Networks". Entropy 23, n.º 1 (18 de janeiro de 2021): 117. http://dx.doi.org/10.3390/e23010117.
Texto completo da fonteAlahmadi, Amani A., Jennifer A. Flegg, Davis G. Cochrane, Christopher C. Drovandi e Jonathan M. Keith. "A comparison of approximate versus exact techniques for Bayesian parameter inference in nonlinear ordinary differential equation models". Royal Society Open Science 7, n.º 3 (março de 2020): 191315. http://dx.doi.org/10.1098/rsos.191315.
Texto completo da fonteSeo, Jung-In, Jae-Woo Jeon e Suk-Bok Kang. "Exact Interval Inference for the Two-Parameter Rayleigh Distribution Based on the Upper Record Values". Journal of Probability and Statistics 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/8246390.
Texto completo da fonteVolaufová, Júlia, e Viktor Witkovský. "On exact inference in linear models with two variance-covariance components". Tatra Mountains Mathematical Publications 51, n.º 1 (1 de novembro de 2012): 173–81. http://dx.doi.org/10.2478/v10127-012-0017-9.
Texto completo da fonteGHAHRAMANI, ZOUBIN. "AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS". International Journal of Pattern Recognition and Artificial Intelligence 15, n.º 01 (fevereiro de 2001): 9–42. http://dx.doi.org/10.1142/s0218001401000836.
Texto completo da fonteFriston, Karl J., Lancelot Da Costa e Thomas Parr. "Some Interesting Observations on the Free Energy Principle". Entropy 23, n.º 8 (19 de agosto de 2021): 1076. http://dx.doi.org/10.3390/e23081076.
Texto completo da fonteUllah, Insha, Sudhir Paul, Zhenjie Hong e You-Gan Wang. "Significance tests for analyzing gene expression data with small sample sizes". Bioinformatics 35, n.º 20 (15 de março de 2019): 3996–4003. http://dx.doi.org/10.1093/bioinformatics/btz189.
Texto completo da fonteHuang, Kai, e Jie Mi. "Inference about Weibull Distribution Using Upper Record Values". International Journal of Reliability, Quality and Safety Engineering 22, n.º 04 (agosto de 2015): 1550016. http://dx.doi.org/10.1142/s0218539315500163.
Texto completo da fonteJaakkola, T. S., e M. I. Jordan. "Variational Probabilistic Inference and the QMR-DT Network". Journal of Artificial Intelligence Research 10 (1 de maio de 1999): 291–322. http://dx.doi.org/10.1613/jair.583.
Texto completo da fonteJiao, Jiajia. "HEAP: A Holistic Error Assessment Framework for Multiple Approximations Using Probabilistic Graphical Models". Electronics 9, n.º 2 (22 de fevereiro de 2020): 373. http://dx.doi.org/10.3390/electronics9020373.
Texto completo da fonteMiller, David J., e Lian Yan. "Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification". Neural Computation 12, n.º 9 (1 de setembro de 2000): 2175–207. http://dx.doi.org/10.1162/089976600300015105.
Texto completo da fonteLin, Peng, Martin Neil e Norman Fenton. "Improved High Dimensional Discrete Bayesian Network Inference using Triplet Region Construction". Journal of Artificial Intelligence Research 69 (27 de setembro de 2020): 231–95. http://dx.doi.org/10.1613/jair.1.12198.
Texto completo da fonteMozer, Reagan, Luke Miratrix, Aaron Russell Kaufman e L. Jason Anastasopoulos. "Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality". Political Analysis 28, n.º 4 (17 de março de 2020): 445–68. http://dx.doi.org/10.1017/pan.2020.1.
Texto completo da fonteZhu, Jianping, Hua Xin, Chenlu Zheng e Tzong-Ru Tsai. "Inference for the Process Performance Index of Products on the Basis of Power-Normal Distribution". Mathematics 10, n.º 1 (23 de dezembro de 2021): 35. http://dx.doi.org/10.3390/math10010035.
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