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Artículos de revistas sobre el tema "Hierarchical variables"

Autor: Grafiati

Publicado: 7 de julio de 2024

Última modificación: 31 de julio de 2025

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1

Shneiberg, I. Ya. "Hierarchical Sequences of Random Variables." Theory of Probability & Its Applications 31, no. 1 (1987): 137–41. http://dx.doi.org/10.1137/1131018.

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2

Yoshimura, Masataka, and Kazuhiro Izui. "Smart Optimization of Machine Systems Using Hierarchical Genotype Representations." Journal of Mechanical Design 124, no. 3 (2002): 375–84. http://dx.doi.org/10.1115/1.1486013.

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Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, however, it is difficult to concurrently optimize all design variables of portions within such hierarchical structures. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations f

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3

Lopera Gonzalez, Luis I., and Oliver Amft. "Mining hierarchical relations in building management variables." Pervasive and Mobile Computing 26 (February 2016): 91–101. http://dx.doi.org/10.1016/j.pmcj.2015.10.009.

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4

Camiz, S., J. Denimal, and V. Pillar. "Hierarchical factor classification of variables in ecology." Community Ecology 7, no. 2 (2006): 165–79. http://dx.doi.org/10.1556/comec.7.2006.2.4.

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5

Wang, Jie, and Xiao Dong Zhu. "Analysis and Application of a Kind of Hierarchical Fuzzy Systems." Advanced Materials Research 219-220 (March 2011): 1097–100. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1097.

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In this paper a kind of hierarchical fuzzy systems was introduced. The characteristics and structural relation of this hierarchical fuzzy system were analyzed. The sensitivity between the input variables and the output variables and the position of variables in the hierarchical fuzzy system were given according to the importance of variables. The weight coefficient of variables was confirmed applying the methods of analytic hierarchical process (AHP). Then the structural analysis and the weight coefficient were applied to the forewarning system of oil drilling.

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6

Pulido-Valdeolivas, I., D. Gómez-Andrés, J. A. Martin, J. López, E. Gómez-Barrena, and E. Rausell. "P6.14 Hierarchical clustering of Gillette Gait Index variables." Clinical Neurophysiology 122 (June 2011): S87. http://dx.doi.org/10.1016/s1388-2457(11)60303-9.

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7

Kristan, William B. "Sources and Expectations for Hierarchical Structure in Bird-habitat Associations." Condor 108, no. 1 (2006): 5–12. http://dx.doi.org/10.1093/condor/108.1.5.

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Abstract Hierarchical structure in bird-habitat associations can arise from hierarchical structure in environmental variables and from the scale-dependent responses of birds to habitat. Hierarchical structure in environmental variables is expected to result from interactions between variables that differ in grain size (spatial resolution) and frequency, and should occur commonly. Birds cannot accurately sample habitat characteristics at all spatial scales simultaneously, and the habitat chosen for a given purpose may differ depending on whether a bird samples from high above the ground (which

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8

Xia, Lu, Meihua Yang, Lang Li, and Xin Zhang. "Aerodynamic Design Based on Global Sensitivity Analysis Method." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (2018): 49–56. http://dx.doi.org/10.1051/jnwpu/20183610049.

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To deal with the problem of the difficult optimization search and expensive computational cost caused by large-scale design variables, the hierarchical optimization design system based on the global sensitivity analysis method is established in this paper. The M-OAT method is used to analyze the global sensitivity of the design variables, according to the sensitivity information to layer design variables, then optimize the design variables in each hierarchy. Through the study of the hierarchical optimization design of airfoils and wings, compared with the normal parameter optimization design s

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9

JIA, WEIJIA, and ZHIBIN SUN. "ON COMPUTATIONAL COMPLEXITY OF HIERARCHICAL OPTIMIZATION." International Journal of Foundations of Computer Science 13, no. 05 (2002): 667–70. http://dx.doi.org/10.1142/s0129054102001369.

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In this work, the computational complexity of a hierarchic optimization problem involving in several players is studied. Each player is assigned with a linear objective function. The set of variables is partitioned such that each subset corresponds to one player as its decision variables. All the players jointly make a decision on the values of these variables such that a set of linear constraints should be satisfied. One special player, called the leader, makes decision on its decision variables before of all the other players. The rest, after learnt of the decision of the leader, make their

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10

Fukuda, Toshio, Yasuhisa Hasegawa, and Koji Shimojima. "Structure Organization of Hierarchical Fuzzy Model Using Genetic Algorithm." Journal of Robotics and Mechatronics 7, no. 1 (1995): 29–35. http://dx.doi.org/10.20965/jrm.1995.p0029.

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This paper proposes a method to organize the hierarchical structure of fuzzy model using the Genetic Algorithm and back-propagation method. The number of fuzzy rules increases exponentially with the number of input variables. Thus, a fuzzy system with many input variables has an extremely large number of fuzzy rules. Hierarchical structure of fuzzy reasoning is one of the methods to reduce the number of fuzzy rules and membership functions. However, it is very difficult to organize the hierarchical structure because the hierarchical structure cannot be constructed without considering the relat

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11

Zhao, Kun, Hongwei Ding, Kai Ye, and Xiaohui Cui. "A Transformer-Based Hierarchical Variational AutoEncoder Combined Hidden Markov Model for Long Text Generation." Entropy 23, no. 10 (2021): 1277. http://dx.doi.org/10.3390/e23101277.

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The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially between the latent variables that control the generation of the sentences. The relationships between these latent variables help in generating continuous and logically connected long texts. There exist very few studies on the relationships between these latent variables. We proposed a method for combining the Transformer-Based Hierarchical Variational A

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12

Asfaw Dagne, Getachew. "Bayesian analysis of hierarchical poisson models with latent variables." Communications in Statistics - Theory and Methods 28, no. 1 (1999): 119–36. http://dx.doi.org/10.1080/03610929908832286.

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13

Hajnal, Istvan, and Geert Loosveldt. "The Sensitivity of Hierarchical Clustering Solutions to Irrelevant Variables." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 50, no. 1 (1996): 56–70. http://dx.doi.org/10.1177/075910639605000105.

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14

You, Heecheon, and Taebeum Ryu. "Development of a Hierarchical Estimation Method for Anthropometric Variables." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 6 (2004): 961–65. http://dx.doi.org/10.1177/154193120404800609.

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15

Gallizo, José L., and Manuel Salvador. "Share prices and accounting variables: a hierarchical Bayesian analysis." Review of Accounting and Finance 5, no. 3 (2006): 268–78. http://dx.doi.org/10.1108/14757700610686813.

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16

Carlson, Kevin, Hanko K. Zeitzmann, and Jerry Flynn. "Add Artifact Control Variables Last in Hierarchical Regression Analyses." Academy of Management Proceedings 2012, no. 1 (2012): 16952. http://dx.doi.org/10.5465/ambpp.2012.16952abstract.

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17

Rockova, Veronika, Emmanuel Lesaffre, Jolanda Luime, and Bob Löwenberg. "Hierarchical Bayesian formulations for selecting variables in regression models." Statistics in Medicine 31, no. 11-12 (2012): 1221–37. http://dx.doi.org/10.1002/sim.4439.

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18

You, Heecheon, and Taebeum Ryu. "Development of a hierarchical estimation method for anthropometric variables." International Journal of Industrial Ergonomics 35, no. 4 (2005): 331–43. http://dx.doi.org/10.1016/j.ergon.2004.09.007.

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19

Shang, Zengqiang, Peiyang Shi, Pengyuan Zhang, Li Wang, and Guangying Zhao. "HierTTS: Expressive End-to-End Text-to-Waveform Using a Multi-Scale Hierarchical Variational Auto-Encoder." Applied Sciences 13, no. 2 (2023): 868. http://dx.doi.org/10.3390/app13020868.

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End-to-end text-to-speech (TTS) models that directly generate waveforms from text are gaining popularity. However, existing end-to-end models are still not natural enough in their prosodic expressiveness. Additionally, previous studies on improving the expressiveness of TTS have mainly focused on acoustic models. There is a lack of research on enhancing expressiveness in an end-to-end framework. Therefore, we propose HierTTS, a highly expressive end-to-end text-to-waveform generation model. It deeply couples the hierarchical properties of speech with hierarchical variational auto-encoders and

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20

Kang, Mincheol. "A proposed improvement to the multilevel theory for hierarchical decision-making teams." Journal of Management & Organization 16, no. 1 (2010): 151–67. http://dx.doi.org/10.1017/s1833367200002339.

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AbstractThe multilevel theory proposed by Hollenbeck et al. identified a set of core variables that are central to accuracy in decision-making in hierarchical teams with distributed expertise. Following the identification of the limitations of the original core variables, a new set of core variables is proposed: (a) member validity, which represents the overall predictability of team members with regard to the correct decision and (b) hierarchical sensitivity, which represents the effectiveness of the leader's weightings of members' recommendations. To test the revised theory, a computational

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21

Kang, Mincheol. "A proposed improvement to the multilevel theory for hierarchical decision-making teams." Journal of Management & Organization 16, no. 1 (2010): 151–67. http://dx.doi.org/10.5172/jmo.16.1.151.

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AbstractThe multilevel theory proposed by Hollenbeck et al. identified a set of core variables that are central to accuracy in decision-making in hierarchical teams with distributed expertise. Following the identification of the limitations of the original core variables, a new set of core variables is proposed: (a) member validity, which represents the overall predictability of team members with regard to the correct decision and (b) hierarchical sensitivity, which represents the effectiveness of the leader's weightings of members' recommendations. To test the revised theory, a computational

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22

Krenke, A. N., Yu G. Puzachenko, and M. Yu Puzachenko. "Spatial organization of regional mesoclimate." Izvestiya Rossiiskoi akademii nauk. Seriya geograficheskaya, no. 3 (June 25, 2019): 116–30. http://dx.doi.org/10.31857/s2587-556620193116-130.

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In this article the method of derivation of the hierarchical levels of organization of climatic variables or regional scale is considered. Based on the Worldclim database, the main integral factors reflecting the variation of climatic variables are identified, and then decomposed into hierarchical levels with different linear dimensions of oscillations. Hierarchical levels are distinguished through the study of the fractal dimensions of different parts of the spectrum of the obtained factors and the isolation of subharmonics on the basis of an analysis of the residues of the fractal model. The

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23

Reise, Steven P., and Naihua Duan. "Multilevel Modeling and its Application in Counseling Psychology Research." Counseling Psychologist 27, no. 4 (1999): 528–51. http://dx.doi.org/10.1177/0011000099274003.

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Multilevel modeling (MLM) should be used when a researcher has collected hierarchical data. For example, when a researcher investigates an outcome variable (e.g., depression) with several clients drawn from different clinicians, the data set has a hierarchical structure. Herein, we describe the use of MLM in counseling research. The goals include the following: (a) to specify research contexts where MLM may be applied, (b) to describe how to conduct data analyses using MLM, and (c) to highlight key statistical and design issues encountered when analyzing hierarchical data. We also highlight ho

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24

Bolnokin, V. E., S. A. Sorokin, D. I. Mutin, et al. "Mathematical model of intelligent decision support based on hierarchical logical constructions." Journal of Physics: Conference Series 2373, no. 5 (2022): 052020. http://dx.doi.org/10.1088/1742-6596/2373/5/052020.

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Abstract The paper is devoted to the investigation of mathematical model of intelligent decision support based on a hierarchical logical construction. Hierarchical logical constructions and forecasting variables are described. The goal of hierarchical logical construction analysis is to get the most accurate forecast possible. Models and algorithms of an intelligent decision support system using a hierarchical logical construction are described.

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25

Deliu, M., S. Yavuz, M. Sperrin, et al. "P120 Challenges in using hierarchical clustering to identify asthma subtypes: choosing the variables and variable transformation." Thorax 71, Suppl 3 (2016): A148.1—A148. http://dx.doi.org/10.1136/thoraxjnl-2016-209333.263.

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26

Fuchs, Sebastian, F. Marta L. Di Lascio, and Fabrizio Durante. "Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables." Computational Statistics & Data Analysis 159 (July 2021): 107201. http://dx.doi.org/10.1016/j.csda.2021.107201.

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27

Romero-Medrano, Lorena, Pablo Moreno-Muñoz, and Antonio Artés-Rodríguez. "Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection." Journal of Signal Processing Systems 94, no. 2 (2021): 215–27. http://dx.doi.org/10.1007/s11265-021-01705-8.

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AbstractBayesian change-point detection, with latent variable models, allows to perform segmentation of high-dimensional time-series with heterogeneous statistical nature. We assume that change-points lie on a lower-dimensional manifold where we aim to infer a discrete representation via subsets of latent variables. For this particular model, full inference is computationally unfeasible and pseudo-observations based on point-estimates of latent variables are used instead. However, if their estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we pr

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28

Joo, Moon-G. "Hierarchical Fuzzy System with only system variables for IF-part." Journal of Korean Institute of Intelligent Systems 14, no. 2 (2004): 178–83. http://dx.doi.org/10.5391/jkiis.2004.14.2.178.

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29

Commons, Michael Lamport, Sagun Giri, and William Joseph Harrigan. "The small effects of non-hierarchical complexity variables on performance." Behavioral Development Bulletin 19, no. 4 (2014): 31–36. http://dx.doi.org/10.1037/h0101079.

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30

Soffritti, Gabriele. "Hierarchical clustering of variables: a comparison among strategies of analysis." Communications in Statistics - Simulation and Computation 28, no. 4 (1999): 977–99. http://dx.doi.org/10.1080/03610919908813588.

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31

Kojadinovic, Ivan. "Agglomerative hierarchical clustering of continuous variables based on mutual information." Computational Statistics & Data Analysis 46, no. 2 (2004): 269–94. http://dx.doi.org/10.1016/s0167-9473(03)00153-1.

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32

Mandozzi, Jacopo, and Peter Bühlmann. "Hierarchical Testing in the High-Dimensional Setting With Correlated Variables." Journal of the American Statistical Association 111, no. 513 (2016): 331–43. http://dx.doi.org/10.1080/01621459.2015.1007209.

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33

Giunta, Gaetano, Salim Belouettar, Olivier Polit, Laurent Gallimard, Philippe Vidal, and Michele D’ottavio. "Hierarchical Beam Finite Elements Based Upon a Variables Separation Method." International Journal of Applied Mechanics 08, no. 02 (2016): 1650026. http://dx.doi.org/10.1142/s1758825116500265.

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A family of hierarchical one-dimensional beam finite elements developed within a variables separation framework is presented. A Proper Generalized Decomposition (PGD) is used to divide the global three-dimensional problem into two coupled ones: one defined on the cross-section space (beam modeling kinematic approximation) and one belonging to the axis space (finite element solution). The displacements over the cross-section are approximated via a Unified Formulation (UF). A Lagrangian approximation is used along the beam axis. The resulting problems size is smaller than that of the classical e

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34

Veltmeyer, Johan, and Sherif Mohamed. "Investigation into the hierarchical nature of TQM variables using structural modelling." International Journal of Quality & Reliability Management 34, no. 4 (2017): 462–77. http://dx.doi.org/10.1108/ijqrm-04-2015-0052.

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Purpose The purpose of this paper is to provide the empirical evidence supporting the existence of a multi-level hierarchical TQM model showing the structural inter-relationships among a total of 16 TQM variables (i.e. drivers, enablers and outcomes). Design/methodology/approach The set of identified TQM variables is the product of an in-depth review of the literature, and a robust reiterative process of verification and validation. Inter-relationships among the TQM variables were subjected to the scrutiny of a panel of experts, and were used as a basis for developing a web-based survey to exp

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35

Çakmak, Mahmut Esat, and Esra Dobrucalı. "Determination of Critical Variables for Resilient Urbanization." Black Sea Journal of Engineering and Science 8, no. 3 (2025): 37–38. https://doi.org/10.34248/bsengineering.1614203.

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As society urbanizes in economic, political, demographic, and sociological terms, living spaces, buildings, streets, avenues, and facilities must also physically adapt to urbanization and form resilient urbanization. The 11. goal of Sustainable Development Goals is defined as "Sustainable Cities and Communities". The goal of this target is to "Make cities and human settlements inclusive, safe, resilient, and sustainable". This target once again highlights the importance of sustainable and resilient cities and urbanization. This study was conducted to identify the fundamental and critical varia

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36

MEURICE, YANNICK. "REMARKS CONCERNING POLYAKOV’S CONJECTURE FOR THE 3D ISING MODEL AND THE HIERARCHICAL APPROXIMATION." Modern Physics Letters A 07, no. 35 (1992): 3331–36. http://dx.doi.org/10.1142/s0217732392002718.

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We consider the possibility of using the hierarchical approximation to understand the continuum limit of a reformulation of the 3D Ising model initiated by Polyakov. We introduce several new formulations of the hierarchical model using dual or fermionic variables. We discuss several aspects of the renormalization group transformation in terms of these new variables. We mention a reformulation of the model closely related to string models proposed by Zabrodin.

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37

DATTA, AJOY K., JERRY L. DERBY, JAMES E. LAWRENCE, and SÉBASTIEN TIXEUIL. "STABILIZING HIERARCHICAL ROUTING." Journal of Interconnection Networks 01, no. 04 (2000): 283–302. http://dx.doi.org/10.1142/s0219265900000172.

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Hierarchical routing provides a less expensive algorithm compared to the traditional all-pairs routing algorithms. We present an algorithm in this paper which benefits from the lower memory requirement, faster routing table lookup, and less costly broadcast exemplified by hierarchical routing, and yet maintains routing capability of all pairs of connected nodes even in the presence of faults, such as link/node failures and repairs, and corruption of program variables. Additionally, this algorithm solves the problem of cluster partitioning where nodes that are supposed to be in the same subset

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38

Stomma, Piotr, and Witold R. Rudnicki. "HCS—hierarchical algorithm for simulation of omics datasets." Bioinformatics 40, Supplement_2 (2024): ii98—ii104. http://dx.doi.org/10.1093/bioinformatics/btae392.

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Abstract Motivation Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducing the dataset to include only those variables that are related to the studied phenomena. Existing simulators of the omics data were mostly developed with the goal of improving the methods for generations of high-quality data, that correspond with the highest possible fidelity to the real level of molecular markers in the biological material

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39

Peterson, H. M., J. L. Nieber, R. Kanivetsky, and B. Shmagin. "Regionalization of landscape characteristics to map hydrologic variables." Journal of Hydroinformatics 16, no. 3 (2013): 633–48. http://dx.doi.org/10.2166/hydro.2013.051.

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By integrating groundwater, surface water and vadose zone systems, the terrestrial hydrologic system can be used to spatially map water balance characteristics spanning local to global scales, even when long-term stream gauge data are unavailable. The Watershed Characteristics Approach (WCA) is a hydrologic estimation model developed using a system-based approach focused on the regionalization of landscape characteristics to define unique hierarchical hydrogeological units (HHUs) and establish their link to hydrologic characteristics. Although the WCA can be used to map any hydrologic variable

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40

Raykov, Tenko, and George A. Marcoulides. "Intraclass Correlation Coefficients in Hierarchical Design Studies With Discrete Response Variables." Educational and Psychological Measurement 75, no. 6 (2015): 1063–70. http://dx.doi.org/10.1177/0013164414564052.

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41

Wang, Di, Xiao-Jun Zeng, and John A. Keane. "Hierarchical hybrid fuzzy-neural networks for approximation with mixed input variables." Neurocomputing 70, no. 16-18 (2007): 3019–33. http://dx.doi.org/10.1016/j.neucom.2006.07.015.

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42

Yap, Dorcas Fen‐Fung, Nasriah Nasir, Karen S. M. Tan, and Lily H. S. Lau. "Variables which predict maternal self‐efficacy: A hierarchical linear regression analysis." Journal of Applied Research in Intellectual Disabilities 32, no. 4 (2019): 841–48. http://dx.doi.org/10.1111/jar.12575.

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43

Schäfer, Martin, Hans-Ulrich Klein, and Holger Schwender. "Integrative analysis of multiple genomic variables using a hierarchical Bayesian model." Bioinformatics 33, no. 20 (2017): 3220–27. http://dx.doi.org/10.1093/bioinformatics/btx356.

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44

Cook, J., and B. Derrida. "Polymers on disordered hierarchical lattices: A nonlinear combination of random variables." Journal of Statistical Physics 57, no. 1-2 (1989): 89–139. http://dx.doi.org/10.1007/bf01023636.

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45

Gibert, Karina, Aïda Valls, and Montserrat Batet. "Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering." Knowledge and Information Systems 40, no. 3 (2013): 559–93. http://dx.doi.org/10.1007/s10115-013-0663-5.

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46

Lefèvre, Claude, and Sergey Utev. "Comparing sums of exchangeable Bernoulli random variables." Journal of Applied Probability 33, no. 2 (1996): 285–310. http://dx.doi.org/10.2307/3215055.

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The paper is first concerned with a comparison of the partial sums associated with two sequences of n exchangeable Bernoulli random variables. It then considers a situation where such partial sums are obtained through an iterative procedure of branching type stopped at the first-passage time in a linearly decreasing upper barrier. These comparison results are illustrated with applications to certain urn models, sampling schemes and epidemic processes. A key tool is a non-standard hierarchical class of stochastic orderings between discrete random variables valued in {0, 1,· ··, n}.

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47

Lefèvre, Claude, and Sergey Utev. "Comparing sums of exchangeable Bernoulli random variables." Journal of Applied Probability 33, no. 02 (1996): 285–310. http://dx.doi.org/10.1017/s0021900200099733.

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The paper is first concerned with a comparison of the partial sums associated with two sequences of n exchangeable Bernoulli random variables. It then considers a situation where such partial sums are obtained through an iterative procedure of branching type stopped at the first-passage time in a linearly decreasing upper barrier. These comparison results are illustrated with applications to certain urn models, sampling schemes and epidemic processes. A key tool is a non-standard hierarchical class of stochastic orderings between discrete random variables valued in {0, 1,· ··, n}.

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48

Myrseth, Inge. "Hierarchical Ensemble Kalman Filter." SPE Journal 15, no. 02 (2010): 569–80. http://dx.doi.org/10.2118/125851-pa.

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Summary This paper presents the hierarchical ensemble Kalman filter (HEnKF) as a robust extension of the ensemble Kalman filter (EnKF). The HEnKF is developed to be robust against features like estimation uncertainty and rank deficiency related to covariance estimation in EnKF. The HEnKF imposes a hierarchical model on the state variables and uses prior distributions from the Gauss conjugate family of distributions to obtain more-robust estimates. An empirical study demonstrates that the HEnKF provides more-reliable results than the traditional EnKF approach. Better predictions and more-realis

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49

ALONSO, SERAFÍN, ANTONIO MORÁN, MIGUEL A. PRADA, PABLO BARRIENTOS, and MANUEL DOMÍNGUEZ. "MONITORING POWER CONSUMPTION USING A GENERALIZED VARIANT OF SELF-ORGANIZING MAP (SOM)." International Journal of Modern Physics B 26, no. 25 (2012): 1246005. http://dx.doi.org/10.1142/s0217979212460058.

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In this paper, we present a new approach for monitoring power consumption in several processes. The generalization of the envSOM algorithm, a variant of Self-Organizing Map (SOM), is used to build an electrical model and visualize the information. The envSOM extended to n hierarchical phases allows us to obtain a more accurate model from real past data. The model is conditioned hierarchically on environmental variables. In this way, time variables can be used to consider seasonality and weekday/hour periodicity. Time variable maps and electrical component planes make it possible to visualize a

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50

Normington, James P., Eric F. Lock, Thomas A. Murray, and Caroline S. Carlin. "Bayesian variable selection in hierarchical difference-in-differences models." Statistical Methods in Medical Research 31, no. 1 (2021): 169–83. http://dx.doi.org/10.1177/09622802211051087.

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A popular method for estimating a causal treatment effect with observational data is the difference-in-differences model. In this work, we consider an extension of the classical difference-in-differences setting to the hierarchical context in which data cannot be matched at the most granular level. Our motivating example is an application to assess the impact of primary care redesign policy on diabetes outcomes in Minnesota, in which the policy is administered at the clinic level and individual outcomes are not matched from pre- to post-intervention. We propose a Bayesian hierarchical differen

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