Relevant bibliographies by topics / Prior informatif

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Prior informatif'

Author: Grafiati
Published: 21 June 2022

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Journal articles on the topic "Prior informatif"

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Hasanah, Uswatul, Ferra Yanuar, and Dodi Devianto. "PENDUGAAN PARAMETER PADA DISTRIBUSI GAMMA DENGAN METODE BAYES." Jurnal Matematika UNAND 7, no. 4 (2019): 81. http://dx.doi.org/10.25077/jmu.7.4.81-86.2018.

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Penelitian ini membahas tentang pendugaan parameter pada distribusi Gamma dengan parameter α diketahui. Metode pendugaan parameter yang digunakan adalah metode Bayes dengan dua distribusi prior, yaitu distribusi prior konjugat dan distribusi prior non-informatif. Distribusi prior konjugat yang diperoleh adalah distribusi Gamma (α , , β, ) dan distribusi prior non-informatif diperoleh dengan melakukan metode perluasan Jeffrey sehingga menghasilkan prior Jeffrey adalah 1 β2k .Kata Kunci: Metode Bayes, Distribusi prior, Metode Jeffrey, Distribusi Gamma
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Rahmadiah, Annisa. "INFERENSI BAYESIAN PADA DISTRIBUSI EKSPONENSIAL." Jurnal Matematika UNAND 7, no. 4 (2019): 93. http://dx.doi.org/10.25077/jmu.7.4.93-99.2018.

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Penelitian ini dilakukan dengan tujuan menduga parameter pada distribusi Eksponensial dengan metode Bayes. Pendugaan parameter dilakukan secara analitik dengan menggunakan distribusi Gamma sebagai prior konjugat dan distribusi prior Jeffrey sebagai prior non-informatif. Setelah itu, dilakukan evaluasi penduga menggunakan metode AIC. Berdasarkan studi analitik diperoleh bahwa distribusi Gamma sebagai distribusi prior konjugat lebih baik dibandingkan dengan prior Jeffrey dalam hal pendugaan parameter.Kata Kunci: Metode Bayes, distribusi prior, distribusi posterior, fungsi likelihood, metode AIC
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Yani, Resti Nanda, Ferra Yanuar, and Hazmira Yozza. "INFERENSI BAYESIAN UNTUK 2 DARI DISTRIBUSI NORMAL DENGAN BERBAGAI DISTRIBUSI PRIOR." Jurnal Matematika UNAND 7, no. 2 (2018): 132. http://dx.doi.org/10.25077/jmu.7.2.132-139.2018.

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Abstrak. Pada penelitian ini dilakukan pendugaan parameter variansi (2) dari dis-tribusi Normal dengan mean () diketahui. Pendugaan parameter variansi (2) terse-but dilakukan secara analitik dengan menggunakan distribusi Invers Gamma sebagaiprior konjugat, metode Jerey sebagai prior non-informatif dan distribusi Uniform se-bagai prior non-konjugat. Pada penelitian ini kriteria evaluasi penduga yang digunakanadalah MSE dan sifat tak bias. Berdasarkan studi analitik diperoleh bahwa distribusiInvers Gamma sebagai prior konjugat merupakan prior terbaik diantara dua distribusiprior lainnya.Kata Kun
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Febriani, Dini, Sugito Sugito, and Alan Prahutama. "ANALISIS METODE BAYESIAN MENGGUNAKAN NON-INFORMATIF PRIOR UNIFORM DISKRIT PADA SISTEM ANTREAN PELAYANAN GERBANG TOL MUKTIHARJO." Jurnal Gaussian 10, no. 3 (2021): 337–45. http://dx.doi.org/10.14710/j.gauss.v10i3.32783.

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The growth rate of the traffic that is high resulting in congestion on the road network system. One of the government's efforts in addressing the issue with the build highways to reduce congestion, especially in large cities. One of the queuing phenomena that often occurs in the city of Semarang is the queue at the Toll Gate Muktiharjo, that the queue of vehicles coming to make toll payment. This study aims to determine how the service system at the Toll Gate Muktiharjo. This can be known by getting a queue system model and a measure of system performance from the distribution of arrival and s
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Fu, Ying, Xi Wu, Xiaohua Li, Kun He, Yi Zhang, and Jiliu Zhou. "Image Motion Restoration Using Fractional-Order Gradient Prior." Informatica 26, no. 4 (2015): 621–34. http://dx.doi.org/10.15388/informatica.2015.67.

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Hyun, Jeong-Hoon. "주관적 성과평가에서 전년도 성과정보가 관대화경향에 미치는 영향". Korean Governmental Accounting Review 19, № 1 (2021): 167–218. http://dx.doi.org/10.15710/kgar.2021.19.1.167.

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Pandey, Vijay Kumar, Rajeev Pandey, and Mayank Trivedi. "Bayesian Method in Linear Model and Constant Time Series Model Using Non-Informative Prior Under Phenology." Mathematical Journal of Interdisciplinary Sciences 3, no. 2 (2015): 183–91. http://dx.doi.org/10.15415/mjis.2015.32016.

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CHANDRA, N., and V. K. RATHAUR. "Bayesian Estimation of Augmented Exponential Strength Reliability Models Under Non-informative Priors." Mathematical Journal of Interdisciplinary Sciences 5, no. 1 (2016): 15–31. http://dx.doi.org/10.15415/mjis.2016.51002.

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Myungjin Cho, Myungjin Cho. "Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information." Chinese Optics Letters 13, no. 7 (2015): 070301–70304. http://dx.doi.org/10.3788/col201513.070301.

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Kalaylioglu, Zeynep, and Haydar Demirhan. "A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements." Statistical Methods in Medical Research 26, no. 6 (2015): 2885–96. http://dx.doi.org/10.1177/0962280215615003.

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Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate varian
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Dissertations / Theses on the topic "Prior informatif"

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Papoutsis, Panayotis. "Potentiel et prévision des temps d'attente pour le covoiturage sur un territoire." Thesis, Ecole centrale de Nantes, 2021. http://www.theses.fr/2021ECDN0059.

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Cette thèse s’intéresse au potentiel et à la prévision des temps d’attente concernant le covoiturage sur un territoire en utilisant des méthodes d’apprentissage statistique. Cinq thèmes principaux sont abordés dans le présent manuscrit. Le premier présente des techniques de régression quantile afin de prédire des temps d’attente. Le deuxième détaille la construction d’un processus de travail empruntant des outils des Systèmes d’Information Géographique (SIG) afin d’exploiter pleinement les données issues du covoiturage. Dans un troisième temps nous construisons un modèle hiérarchique bayésien
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Bioche, Christèle. "Approximation de lois impropres et applications." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22626/document.

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Le but de cette thèse est d’étudier l’approximation d’a priori impropres par des suites d’a priori propres. Nous définissons un mode de convergence sur les mesures de Radon strictement positives pour lequel une suite de mesures de probabilité peut admettre une mesure impropre pour limite. Ce mode de convergence, que nous appelons convergence q-vague, est indépendant du modèle statistique. Il permet de comprendre l’origine du paradoxe de Jeffreys-Lindley. Ensuite, nous nous intéressons à l’estimation de la taille d’une population. Nous considérons le modèle du removal sampling. Nous établissons
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Pohl, Kilian Maria. "Prior information for brain parcellation." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33925.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.<br>Includes bibliographical references (p. 171-184).<br>To better understand brain disease, many neuroscientists study anatomical differences between normal and diseased subjects. Frequently, they analyze medical images to locate brain structures influenced by disease. Many of these structures have weakly visible boundaries so that standard image analysis algorithms perform poorly. Instead, neuroscientists rely on manual procedures, which are time consuming and increase risks rel
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Ahmed, Syed Ejaz Carleton University Dissertation Mathematics. "Estimation strategies under uncertain prior information." Ottawa, 1987.

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Sunmola, Funlade Tajudeen. "Optimising learning with transferable prior information." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/3983/.

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This thesis addresses the problem of how to incorporate user knowledge about an environment, or information acquired during previous learning in that environment or a similar one, to make future learning more effective. The problem is tackled within the framework of learning from rewards while acting in a Markov Decision Process (MDP). Appropriately incorporating user knowledge and prior experience into learning should lead to better performance during learning (the exploitation-exploration trade-off), and offer a better solution at the end of the learning period. We work in a Bayesian setting
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Ren, Shijie. "Using prior information in clinical trial design." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555104.

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A current concern in medical research is low productivity In the pharmaceutical industry. Failure rates of Phase III clinical trials are high, and this is very costly in terms of using resources and money. Our aim in this thesis is to incorporate prior information in clinical trial design and develop better assessments of the chances of successful clinical trials, so that trial sponsors can improve their success rates. Assurance calculations, which take into account uncertainty about how effective the treatment actually is, provide a more reliable assessment of the probability of a successful
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Parsley, M. P. "Simultaneous localisation and mapping with prior information." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1318103/.

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This thesis is concerned with Simultaneous Localisation and Mapping (SLAM), a technique by which a platform can estimate its trajectory with greater accuracy than odometry alone, especially when the trajectory incorporates loops. We discuss some of the shortcomings of the "classical" SLAM approach (in particular EKF-SLAM), which assumes that no information is known about the environment a priori. We argue that in general this assumption is needlessly stringent; for most environments, such as cities some prior information is known. We introduce an initial Bayesian probabilistic framework which
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Viggh, Herbert E. M. "Surface Prior Information Reflectance Estimation (SPIRE) algorithms." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/17564.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.<br>Includes bibliographical references (p. 393-396).<br>In this thesis we address the problem of estimating changes in surface reflectance in hyperspectral image cubes, under unknown multiplicative and additive illumination noise. Rather than using the Empirical Line Method (ELM) or physics-based approaches, we assumed the presence of a prior reflectance image cube and ensembles of typical multiplicative and additive illumination noise vectors, and developed algorithms which esti
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Ghadermarzy, Navid. "Using prior support information in compressed sensing." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44912.

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Compressed sensing is a data acquisition technique that entails recovering estimates of sparse and compressible signals from n linear measurements, significantly fewer than the signal ambient dimension N. In this thesis we show how we can reduce the required number of measurements even further if we incorporate prior information about the signal into the reconstruction algorithm. Specifically, we study certain weighted nonconvex Lp minimization algorithms and a weighted approximate message passing algorithm. In Chapter 1 we describe compressed sensing as a practicable signal acquisition meth
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Liu, Yang. "Application of prior information to discriminative feature learning." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/285558.

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Learning discriminative feature representations has attracted a great deal of attention since it is a critical step to facilitate the subsequent classification, retrieval and recommendation tasks. In this dissertation, besides incorporating prior knowledge about image labels into the image classification as most prevalent feature learning methods currently do, we also explore some other general-purpose priors and verify their effectiveness in the discriminant feature learning. As a more powerful representation can be learned by implementing such general priors, our approaches achieve state-of-
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Books on the topic "Prior informatif"

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Shavell, Steven. Acquisition and disclosure of information prior to economic exchange. Harvard Law School, 1991.

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Machiels-Bongaerts, Maureen. Mobilizing prior knowledge in text processing: The selective-attention hypothesis versus the cognitive set-point hypothesis. Universitaire Pers Maastricht, 1993.

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Ontario Council of Regents for Colleges of Applied Arts and Technology. Prior Learning Assessment Steering Committee. Information systems for prior learning assessment support: Discussion paper. Canadore College, 1994.

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United States. President's National Security Telecommunications Advisory Committee. Issue review: A review of NSTAC issues addressed prior to NSTAC XIX. The Committee, 1997.

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Office, General Accounting. Government consultants: Agencies' consulting services contract obligations for fiscal year 1987 : fact sheet for the Honorable David Pryor, Chairman, Subcommittee on Federal Services, Post Office, and Civil Service, Committee on Governmental Affairs, U.S. Senate. The Office, 1988.

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Golan, Amos. Prior Information. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0008.

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In this chapter I introduce and quantify prior information and show how to incorporate it into the info-metrics framework. The priors developed arise from fundamental properties of the system, from logical reasoning, or from empirical observations. I start the chapter with the derivation of priors for discrete distributions, which can be handled via the grouping property, and a detailed derivation of surprisal analysis. Constructing priors for continuous distributions is more challenging. That problem is tackled via the method of transformation groups, which is related to the mathematical conc
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Venegas-Martinez, Francisco. Some studies on information measures and prior distributions. 1988.

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Prado, Raquel. Multistate models for mental fatigue. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.29.

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This article discusses the use of structured, multivariate Bayesian dynamic models in the analysis of experimental data involving large-scale electroencephalography (EEG) signals or time series generated on individuals subject to tasks inducing mental fatigue. It first provides an overview of the goals and challenges in the analysis of brain signals, using the EEG case as example, before describing the development and application of novel time-varying autoregressive and regime switching models, which incorporate relevant prior information via structured priors and fitted using novel, customize
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K, Srull Thomas, Wyer Robert S, and Smith Eliot R, eds. Content and process specificity in the effects of prior experiences. Lawrence Erlbaum Associates, 1990.

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Kenetsu hoso: Senji janarizumu shishi. Keyaki Shuppan, 1995.

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Book chapters on the topic "Prior informatif"

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Blasco, Agustín. "Prior Information." In Bayesian Data Analysis for Animal Scientists. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54274-4_9.

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Lambert, Paul B. "Prior Information Conditions." In Essential Introduction to Understanding European Data Protection Rules. Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781138069848-10.

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Lambert, Paul B. "Prior Information Conditions." In Essential Introduction to Understanding European Data Protection Rules. Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315115269-10.

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Robert, Christian P. "From Prior Information to Prior Distributions." In Springer Texts in Statistics. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4314-2_3.

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Walter, Gero, and Frank P. A. Coolen. "Sets of Priors Reflecting Prior-Data Conflict and Agreement." In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40596-4_14.

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O’Donnell, R. T., A. E. Nicholson, B. Han, K. B. Korb, M. J. Alam, and L. R. Hope. "Causal Discovery with Prior Information." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_141.

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Berger, James O. "Prior Information and Subjective Probability." In Springer Series in Statistics. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4757-4286-2_3.

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Feng, Qinrong, and Duoqian Miao. "Structured Prior Knowledge and Granular Structures." In Brain Informatics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04954-5_22.

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John, Jacob, and Prabu Sevugan. "Image Dehazing Through Dark Channel Prior and Color Attenuation Prior." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88244-0_15.

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Xie, Boyi, and Rebecca J. Passonneau. "Supervised HDP Using Prior Knowledge." In Natural Language Processing and Information Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31178-9_21.

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Conference papers on the topic "Prior informatif"

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Chen, Yuanfang, Noel Crespi, Lin Lv, Mingchu Li, Antonio M. Ortiz, and Lei Shu. "Locating using prior information." In SIGCOMM'13: ACM SIGCOMM 2013 Conference. ACM, 2013. http://dx.doi.org/10.1145/2486001.2491688.

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Parsley, Martin P., and Simon J. Julier. "Exploiting prior information in GraphSLAM." In 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2011. http://dx.doi.org/10.1109/icra.2011.5979628.

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von Borries, R., C. Jacques Miosso, and C. Potes. "Compressed Sensing Using Prior Information." In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. IEEE, 2007. http://dx.doi.org/10.1109/camsap.2007.4497980.

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"Optimizing ICA Using Prior Information." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001195800270034.

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Snoussi, Hichem. "Information geometry and prior selection." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 22nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2003. http://dx.doi.org/10.1063/1.1570549.

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Shergadwala, Murtuza N., and Jitesh H. Panchal. "Human Inductive Biases in Design Decision Making." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22252.

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Abstract Designers make information acquisition decisions, such as where to search and when to stop the search. Such decisions are typically made sequentially, such that at every search step designers gain information by learning about the design space. However, when designers begin acquiring information, their decisions are primarily based on their prior knowledge. Prior knowledge influences the initial set of assumptions that designers use to learn about the design space. These assumptions are collectively termed as inductive biases. Identifying such biases can help us better understand how
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Trnka, Pavel, and Vladimir Havlena. "Subspace identification method incorporating prior information." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434236.

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Ravana, Sri Devi, Laurence A. Park, and Alistair Moffat. "System scoring using partial prior information." In the 32nd international ACM SIGIR conference. ACM Press, 2009. http://dx.doi.org/10.1145/1571941.1572129.

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Jiang, Bo, Ming Li, and Ravi Tandon. "Local Information Privacy with Bounded Prior." In ICC 2019 - 2019 IEEE International Conference on Communications (ICC). IEEE, 2019. http://dx.doi.org/10.1109/icc.2019.8761176.

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Day, Peter S., and Peter Bladon. "Using prior information to enhance tracking." In Defense and Security, edited by Michael K. Masten and Larry A. Stockum. SPIE, 2004. http://dx.doi.org/10.1117/12.543623.

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Reports on the topic "Prior informatif"

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ROJAS, Temistocles, Vasily DEMYANOV, Mike CHRISTIE, and Dan ARNOLD. Use of Geological Prior Information in Reservoir. Cogeo@oeaw-giscience, 2011. http://dx.doi.org/10.5242/iamg.2011.0093.

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Baumeister, Christiane, and James Hamilton. Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w20741.

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Hughett, Paul William. Algorithms for biomagnetic source imaging with prior anatomical and physiological information. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/195677.

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Hughett, P. Tradeoffs between measurement residual and reconstruction error in inverse problems with prior information. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/106621.

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Marthaler, Daniel, Andrea L. Bertozzi, and Ira B. Schwartz. Levy Searches Based on A Priori Information: The Biased Levy Walk. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada638319.

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Vessella, Robert L. Does the Phenotyping of Disseminated Prostate Cancer Cells in Blood and Bone Marrow Prior to Radical Prostatectomy Provide Prognostic Information? Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada435227.

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Vessella, Robert L. Does the Phenotyping of Disseminated Prostate Cancer Cells in Blood and Bone Marrow Prior to Radical Prostatectomy Provide Prognostic Information? Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada412293.

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Vessella, Robert. Does the Phenotyping of Disseminated Prostate Cancer Cells in Blood and Bone Marrow Prior to Radical Prostatectomy Provide Prognostic Information? Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada418201.

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Henseler, Sean P. Addressing the Legal Challenges of Network Centric Warfare. Case In Point: The Legal Implications of Obtaining an Information and Knowledge Advantage" Prior to Hostilities". Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada389662.

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Kharel, Arjun, Sudhir Shrestha, Sadikshya Bhattarai, Pauline Oosterhoff, and Karen Snyder. Assessment of Outreach and Engagement with Prospective Migrants by the Agencies Recruiting Labourers for Foreign Employment. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/ids.2022.037.

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This study was conducted to identify the gaps in policies and practices of labour recruitment in Nepal and assess the outreach and engagement of major formal labour intermediaries, private recruitment agencies (PRAs) and pre-departure orientation training (PDOT) centres, with migrant workers for providing information on human trafficking prior to departure. The study used data from interviews with the management of 15 PRAs and 10 PDOT centres, along with a review of online materials published by the sampled PRAs and PDOT centres and existing publications on labour migration from Nepal.
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