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Journal articles on the topic 'Graphical model'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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1

Li, Ying, and Ye Tang. "Design on Intelligent Feature Graphics Based on Convolution Operation." Mathematics 10, no. 3 (January 26, 2022): 384. http://dx.doi.org/10.3390/math10030384.

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With the development and application of artificial intelligence, the technical methods of intelligent image processing and graphic design need to be explored to realize the intelligent graphic design based on traditional graphics such as pottery engraving graphics. An optimized method is aimed to be explored to extract the image features from traditional engraving graphics on historical relics and apply them into intelligent graphic design. For this purpose, an image feature extracted model based on convolution operation is proposed. Parametric test and effectiveness research are conducted to evaluate the performance of the proposed model. Theoretical and practical research shows that the image-extracted model has a significant effect on the extraction of image features from traditional engraving graphics because the image brightness processing greatly simplifies the process of image feature extraction, and the convolution operation improves the accuracy. Based on the brightness feature map output from the proposed model, the design algorithm of intelligent feature graphic is presented to create the feature graphics, which can be directly applied to design the intelligent graphical interface. Taking some pottery engraving graphics from the Neolithic Age as an example, we conduct the practice on image feature extraction and feature graphic design, the results of which further verify the effectiveness of the proposed method. This paper provides a theoretical basis for the application of traditional engraving graphics in intelligent graphical interface design for AI products such as smart tourism products, smart museums, and so on.

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Сальков and Nikolay Sal'kov. "Geometric Simulation and Descriptive Geometry." Geometry & Graphics 4, no. 4 (December 19, 2016): 31–40. http://dx.doi.org/10.12737/22841.

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Geometric simulation is creation of a geometric model, whose properties and characteristics in a varying degree determine the subject of investigation’s properties and characteristics. The geometric model is a special case of the mathematical model. The feature of the geometric model is that it will always be a geometric figure, and therefore, by its very nature, is visual. If the mathematical model is a set of equations, which says little to an ordinary engineer, the geometric model as representation of the mathematical model and as the geometric figure itself, enables to "see" this set. Any geometric model can be represented graphically. Graphical model of an object is a mapping of its geometric model onto a plane (or other surface). Therefore, the graphical model can be considered as a special case of the geometric model. Graphical models are very various – these are graphics, and graphical structures of immense complexity, reflecting spatial geometric figures. These are drawings of geometric figures, simulating processes of all kinds. The simulation goes on as follows. According to known geometric and differential criteria the geometric model is executed. Then a mathematical model is composed based on the geometric model, finally a computer program is compiled on the mathematical model. As a result of consideration in this paper the process of obtaining the geometric models of surface and linear forms for auto-roads it is possible to make a following conclusion. For geometric simulation and the consequent mathematical one the descriptive geometry involvement is vital. Just the descriptive geometry is used both on the initial and final stages of design.

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Alsaade, Fawaz. "Symmetric Crypto-Graphical Model." Trends in Applied Sciences Research 5, no. 2 (February 1, 2010): 146–51. http://dx.doi.org/10.3923/tasr.2010.146.151.

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Tarantola, Claudia. "MCMC model determination for discrete graphical models." Statistical Modelling: An International Journal 4, no. 1 (April 2004): 39–61. http://dx.doi.org/10.1191/1471082x04st063oa.

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Mendling, Jan, Jan Recker, and Hajo A. Reijers. "On the Usage of Labels and Icons in Business Process Modeling." International Journal of Information System Modeling and Design 1, no. 2 (April 2010): 40–58. http://dx.doi.org/10.4018/jismd.2010040103.

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The value of business process models is dependent on the choice of graphical elements in the model and their annotation with additional textual and graphical information. This research discusses the use of text and icons for labeling the graphical constructs in a process model. The authors use two established verb classification schemes to examine the choice of activity labels in process modeling practice. Based on the author’s findings, this paper synthesizes a set of twenty-five activity label categories. Proposed is a systematic approach for graphically representing these label categories through the use of graphical icons, such that the resulting process models are easier and more readily understandable by end users. The author’s findings contribute to an ongoing stream of research investigating the practice of process modeling and thereby contribute to the body of knowledge about conceptual modeling quality overall.

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Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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Akselrud, Lev, and Yuri Grin. "WinCSD: software package for crystallographic calculations (Version 4)." Journal of Applied Crystallography 47, no. 2 (March 11, 2014): 803–5. http://dx.doi.org/10.1107/s1600576714001058.

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The fourth version of the program packageWinCSDis multi-purpose computer software for crystallographic calculations using single-crystal and powder X-ray and neutron diffraction data. The software environment and the graphical user interface are built using the platform of the Microsoft .NET Framework, which grants independence from changing Windows operating systems and allows for transferring to other operating systems. Graphic applications use the three-dimensional OpenGL graphics language.WinCSDcovers the complete spectrum of crystallographic calculations, including powder diffraction pattern deconvolution, crystal structure solution and refinement in 3 + dspace, refinement of the multipole model and electron density studies from diffraction data, and graphical representation of crystallographic information.

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Smirnov, M. V., and R. S. Tolmasov. "Graphical Notation for Document Database Modeling." Open Education 25, no. 5 (November 8, 2021): 50–60. http://dx.doi.org/10.21686/1818-4243-2021-5-50-60.

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Goals and objectives. Graphical models have proven to be a reliable, clear and convenient tool for creating sketch models of databases. Most of the existing notations are designed for the relational data model, the dominant data model for the last thirty years. However, the development of information technologies has led to an increase in the popularity of non-relational data models, primarily the document model. One of the problems of its application in practice is the lack of suitable tools that allow performing graphical modeling of the database, taking into account the features of the document model, at the stage of logical design. The development of appropriate tools is an important and actual task, since their application in practical research makes it possible to identify, classify and analyze typical modeling errors that allow the designer to reduce the risk of their occurrence in the future. The purpose of this article is to develop a graphical notation that, on the one hand, providing convenience for the designer, and on the other hand, taking into account the peculiarities of creating and functioning of the noSQL document storage model.Materials and methods. The materials for the study were numerous publications devoted to the development of graphical notations in problems and their application to database design for various information systems. The selected materials were analyzed and the main graphical notations used to describe the relational data model were identified. Three notations were selected from them, a set of graphic stereotypes, which were most different from each other, the analysis of which allowed us to identify the main image patterns of the components of the relational model.The resulting patterns were applied to the main elements of the document database, which were obtained by analyzing the documentation of the popular MongoDB DBMS.Results. The result of the research was the creation of a new tool for modeling document databases at the logical level, which consists of a set of graphic stereotypes and rules for their application. On the one hand, the development is well known to practitioners who have previously worked with relational data models, since its development took into account many years of experience in using graphical models in the field of relational database design, and on the other hand, it reflects the features of the structure of the document model.Conclusion. The practical application of the developed model has shown the convenience of its use both in the process of designing document databases and in the process of teaching students within this subject area. The use of graphical models constructed in the proposed graphical notation will allow researchers to create and illustrate typical patterns of document databases, which will undoubtedly have a positive impact on the dynamics of the development of promising data storage technologies.

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Lotsi, Anani, and Ernst Wit. "Sparse Gaussian graphical mixture model." Afrika Statistika 11, no. 2 (December 1, 2016): 1041–59. http://dx.doi.org/10.16929/as/2016.1041.91.

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Robinson, Peter. "A model for graphical interaction." Software Engineering Journal 3, no. 6 (1988): 263. http://dx.doi.org/10.1049/sej.1988.0034.

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Yuan, Xiao-Tong, and Tong Zhang. "Partial Gaussian Graphical Model Estimation." IEEE Transactions on Information Theory 60, no. 3 (March 2014): 1673–87. http://dx.doi.org/10.1109/tit.2013.2296784.

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12

Amit, Y., and A. Kong. "Graphical templates for model registration." IEEE Transactions on Pattern Analysis and Machine Intelligence 18, no. 3 (March 1996): 225–36. http://dx.doi.org/10.1109/34.485529.

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Giudici, P. "Decomposable graphical Gaussian model determination." Biometrika 86, no. 4 (December 1, 1999): 785–801. http://dx.doi.org/10.1093/biomet/86.4.785.

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Rong Chen and E. H. Herskovits. "Graphical-model-based morphometric analysis." IEEE Transactions on Medical Imaging 24, no. 10 (October 2005): 1237–48. http://dx.doi.org/10.1109/tmi.2005.854305.

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Denev, Alexander, Adrien Papaioannou, and Orazio Angelini. "A probabilistic graphical models approach to model interconnectedness." International Journal of Risk Assessment and Management 23, no. 2 (2020): 119. http://dx.doi.org/10.1504/ijram.2020.10028855.

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Denev, Alexander, Adrien Papaioannou, and Orazio Angelini. "A probabilistic graphical models approach to model interconnectedness." International Journal of Risk Assessment and Management 23, no. 2 (2020): 119. http://dx.doi.org/10.1504/ijram.2020.106963.

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17

Carvalho, C. M., and J. G. Scott. "Objective Bayesian model selection in Gaussian graphical models." Biometrika 96, no. 3 (May 4, 2009): 497–512. http://dx.doi.org/10.1093/biomet/asp017.

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18

Liu, Zhe. "Bayesian model-averaged regularization for Gaussian graphical models." Communications in Statistics - Simulation and Computation 46, no. 4 (December 20, 2016): 3213–23. http://dx.doi.org/10.1080/03610918.2015.1080837.

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Sciandra, M., and A. Plaia. "A graphical model selection tool for mixed models." Communications in Statistics - Simulation and Computation 47, no. 9 (August 23, 2017): 2624–38. http://dx.doi.org/10.1080/03610918.2017.1353617.

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Birmpa, Panagiota, Jinchao Feng, Markos A. Katsoulakis, and Luc Rey-Bellet. "Model Uncertainty and Correctability for Directed Graphical Models." SIAM/ASA Journal on Uncertainty Quantification 10, no. 4 (October 31, 2022): 1461–512. http://dx.doi.org/10.1137/21m1434453.

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Li, Qiong, Xin Gao, and Hélène Massam. "Bayesian model selection approach for coloured graphical Gaussian models." Journal of Statistical Computation and Simulation 90, no. 14 (June 30, 2020): 2631–54. http://dx.doi.org/10.1080/00949655.2020.1784175.

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22

Wedelin, Dag. "Efficient estimation and model selection in large graphical models." Statistics and Computing 6, no. 4 (December 1996): 313–23. http://dx.doi.org/10.1007/bf00143552.

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Murray, K., S. Heritier, and S. Müller. "Graphical tools for model selection in generalized linear models." Statistics in Medicine 32, no. 25 (May 29, 2013): 4438–51. http://dx.doi.org/10.1002/sim.5855.

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Boiroju, Naveen Kumar. "A Graphical Method for Model Selection." Pakistan Journal of Statistics and Operation Research 8, no. 4 (November 8, 2012): 767. http://dx.doi.org/10.18187/pjsor.v8i4.427.

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Monson, Christopher K., and Kevin D. Seppi. "A Graphical Model for Evolutionary Optimization." Evolutionary Computation 16, no. 3 (September 2008): 289–313. http://dx.doi.org/10.1162/evco.2008.16.3.289.

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We present a statistical model of empirical optimization that admits the creation of algorithms with explicit and intuitively defined desiderata. Because No Free Lunch theorems dictate that no optimization algorithm can be considered more efficient than any other when considering all possible functions, the desired function class plays a prominent role in the model. In particular, this provides a direct way to answer the traditionally difficult question of what algorithm is best matched to a particular class of functions. Among the benefits of the model are the ability to specify the function class in a straightforward manner, a natural way to specify noisy or dynamic functions, and a new source of insight into No Free Lunch theorems for optimization.

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Wu, Changjing, Hongyu Zhao, Huaying Fang, and Minghua Deng. "Graphical model selection with latent variables." Electronic Journal of Statistics 11, no. 2 (2017): 3485–521. http://dx.doi.org/10.1214/17-ejs1331.

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Höhna, Sebastian, Tracy A. Heath, Bastien Boussau, Michael J. Landis, Fredrik Ronquist, and John P. Huelsenbeck. "Probabilistic Graphical Model Representation in Phylogenetics." Systematic Biology 63, no. 5 (June 20, 2014): 753–71. http://dx.doi.org/10.1093/sysbio/syu039.

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Zareifard, Hamid, Håvard Rue, Majid Jafari Khaledi, and Finn Lindgren. "A skew Gaussian decomposable graphical model." Journal of Multivariate Analysis 145 (March 2016): 58–72. http://dx.doi.org/10.1016/j.jmva.2015.08.011.

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29

Bilmes, J. A., and C. Bartels. "Graphical model architectures for speech recognition." IEEE Signal Processing Magazine 22, no. 5 (September 2005): 89–100. http://dx.doi.org/10.1109/msp.2005.1511827.

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30

Gero, J. S., and J. C. Damski. "A symbolic model for graphical emergence." Environment and Planning B: Planning and Design 24, no. 4 (1997): 509–26. http://dx.doi.org/10.1068/b240509.

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31

Peusner, Leonardo. "Premetric thermodynamics. A topological graphical model." Journal of the Chemical Society, Faraday Transactions 2 81, no. 8 (1985): 1151. http://dx.doi.org/10.1039/f29858101151.

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32

Javidian, Mohammad Ali, Zhiyu Wang, Linyuan Lu, and Marco Valtorta. "On a hypergraph probabilistic graphical model." Annals of Mathematics and Artificial Intelligence 88, no. 9 (July 10, 2020): 1003–33. http://dx.doi.org/10.1007/s10472-020-09701-7.

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Donovan, John, and Eamonn Murphy. "Reliability growth—a new graphical model." Quality and Reliability Engineering International 15, no. 3 (May 1999): 167–74. http://dx.doi.org/10.1002/(sici)1099-1638(199905/06)15:3<167::aid-qre219>3.0.co;2-1.

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Saeedi, Ardavan, Yuria Utsumi, Li Sun, Kayhan Batmanghelich, and Li-wei Lehman. "Knowledge Distillation via Constrained Variational Inference." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 8132–40. http://dx.doi.org/10.1609/aaai.v36i7.20786.

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Knowledge distillation has been used to capture the knowledge of a teacher model and distill it into a student model with some desirable characteristics such as being smaller, more efficient, or more generalizable. In this paper, we propose a framework for distilling the knowledge of a powerful discriminative model such as a neural network into commonly used graphical models known to be more interpretable (e.g., topic models, autoregressive Hidden Markov Models). Posterior of latent variables in these graphical models (e.g., topic proportions in topic models) is often used as feature representation for predictive tasks. However, these posterior-derived features are known to have poor predictive performance compared to the features learned via purely discriminative approaches. Our framework constrains variational inference for posterior variables in graphical models with a similarity preserving constraint. This constraint distills the knowledge of the discriminative model into the graphical model by ensuring that input pairs with (dis)similar representation in the teacher model also have (dis)similar representation in the student model. By adding this constraint to the variational inference scheme, we guide the graphical model to be a reasonable density model for the data while having predictive features which are as close as possible to those of a discriminative model. To make our framework applicable to a wide range of graphical models, we build upon the Automatic Differentiation Variational Inference (ADVI), a black-box inference framework for graphical models. We demonstrate the effectiveness of our framework on two real-world tasks of disease subtyping and disease trajectory modeling.

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Pejovic, Branko, Ljubica Vasiljevic, Vladan Micic, and Mitar Perusic. "Suitable model for the calculation of the correlation between the real and the average specific heat capacity and possibilities of its application." Chemical Industry 67, no. 3 (2013): 495–511. http://dx.doi.org/10.2298/hemind111104092p.

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Starting from the definition of the average specific heat capacity for chosen temperature range, the analytic dependence between the real and the mean specific heat capacities is obtained using differential and integral calculation. The obtained relation in differential form for the defined temperature range allows for the problem to be solved directly, without any special restrictions on its use. Using the obtained relation, a general model in the form of a polynomial of arbitrary degree in the function of temperature was derived, which has more suitable and faster practical application and is more general in character than the existing model. New graphical method for solving the problem is obtained based on differential geometry and using the derived equation. This may also have practical significance since many problems in thermodynamics are solved analytically and graphically. This result was used in order to obtain the amount of specific heat exchanged using an analytical model or a planimetric method. In addition, this graphical solution was used for the construction of the diagram showing the dependence between the specific heat exchanged and temperature. This diagram also gives a simple graphical procedure for the calculation of the real and the average specific heat capacity for arbitrary temperature or temperature interval. The confirmation for all graphic constructions is obtained using the differential properties between thermodynamic units. In order for the graphical solutions presented to be applicable in practice, suitable ratio coefficients have been determined for all cases. Verification of the model presented, as well as the possibilities of its application, were given using several characteristic examples of semi-ideal and real gas. Apart from linear and non-linear functions in the form of polynomials, the exponential function of the dependence between specific heat capacities and temperature was also analysed in this process.

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Usenko, Valery, Olga Kodak, and Iryna Usenko. "Geometric reliability model of the five site redundant structure." Engineering review 40, no. 2 (April 1, 2020): 10–15. http://dx.doi.org/10.30765/er.40.2.02.

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The study results from the graphical representation on connectivity probability of systems that have structural redundancy for modeling. The structural reliability of different systems is presented. A oneparameter expression of a two-ring structure reliability with five sites is described. All the unique operating conditions of the structure are shown in a graphical form. The compact form of the exact expression of the redundant two-ring structure reliability with five sites is presented, which is convenient for computer modeling. The peculiarities of the dependences of many variables in the reliability model of the two-ring structure with five sites and four nodes are determined. It is shown that the graphic representation of the dependence of the five variables helps to study the properties of multiparameter dependencies. The components of a geometric model of various dimensions are considered in detail.

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Wan, Jiang, and Nicholas Zabaras. "A probabilistic graphical model based stochastic input model construction." Journal of Computational Physics 272 (September 2014): 664–85. http://dx.doi.org/10.1016/j.jcp.2014.05.002.

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Yuan, M., and Y. Lin. "Model selection and estimation in the Gaussian graphical model." Biometrika 94, no. 1 (February 7, 2007): 19–35. http://dx.doi.org/10.1093/biomet/asm018.

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D’Ambrosio, Donato, Giuseppe Filippone, Rocco Rongo, William Spataro, and Giuseppe A. Trunfio. "Cellular Automata and GPGPU." International Journal of Grid and High Performance Computing 4, no. 3 (July 2012): 30–47. http://dx.doi.org/10.4018/jghpc.2012070102.

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This paper presents an efficient implementation of the SCIARA Cellular Automata computational model for simulating lava flows using the Compute Unified Device Architecture (CUDA) interface developed by NVIDIA and carried out on Graphical Processing Units (GPU). GPUs are specifically designated for efficiently processing graphic data sets. However, they are also recently being exploited for achieving excellent computational results for applications non-directly connected with Computer Graphics. The authors show an implementation of SCIARA and present results referred to a Tesla GPU computing processor, a NVIDIA device specifically designed for High Performance Computing, and a Geforce GT 330M commodity graphic card. Their carried out experiments show that significant performance improvements are achieved, over a factor of 100, depending on the problem size and type of performed memory optimization. Experiments have confirmed the effectiveness and validity of adopting graphics hardware as an alternative to expensive hardware solutions, such as cluster or multi-core machines, for the implementation of Cellular Automata models.

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Madigan, David, and Adrian E. Raftery. "Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window." Journal of the American Statistical Association 89, no. 428 (December 1994): 1535–46. http://dx.doi.org/10.1080/01621459.1994.10476894.

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41

Daka, Trishala, Vineesha Doppalapudi, and Venkata Dilip Kumar Pasupuleti. "Building Information Model: A Graphical User Interface to Generate a three Dimensional Building." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-ICAEIT2017 (November 30, 2018): 145–50. http://dx.doi.org/10.31142/ijtsrd19125.

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Massa, M. Sofia, and Monica Chiogna. "Effectiveness of combinations of Gaussian graphical models for model building." Journal of Statistical Computation and Simulation 83, no. 9 (September 2013): 1602–12. http://dx.doi.org/10.1080/00949655.2012.667216.

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Krusińska, E. "Suitable Location Model Selection in the Terminology of Graphical Models." Biometrical Journal 32, no. 7 (January 19, 2007): 817–26. http://dx.doi.org/10.1002/bimj.4710320707.

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Bathgate, Ronald H. "Studies of Translation Models 3 : An Interaction Model of the Translation Process." Meta 30, no. 2 (September 30, 2002): 129–38. http://dx.doi.org/10.7202/002000ar.

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Abstract A graphical notation permitting a detailed description of the translation process is developed and used to describe a number of different translation situations. This notation, based on a recently published model of the translation process, lays particular emphasis on the interactive nature of the translation process, i.e. on the fact that a translator does not work in isolation but is a member of a team whose task is to produce an optimum translation by joint effort. A written notation equivalent to the graphical notation is also briefly introduced.

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Buntine, W. L. "Operations for Learning with Graphical Models." Journal of Artificial Intelligence Research 2 (December 1, 1994): 159–225. http://dx.doi.org/10.1613/jair.62.

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This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, andthe manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximizationalgorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decompositiontechniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

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Vuffray, Marc, Sidhant Misra, and Andrey Y. Lokhov. "Efficient learning of discrete graphical models*." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (December 1, 2021): 124017. http://dx.doi.org/10.1088/1742-5468/ac3aea.

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Abstract Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for learning graphical models with least amount of data remains an active research topic. Reconstruction of graphical models that describe the statistics of discrete variables is a particularly challenging problem, for which the maximum likelihood approach is intractable. In this work, we provide the first sample-efficient method based on the interaction screening framework that allows one to provably learn fully general discrete factor models with node-specific discrete alphabets and multi-body interactions, specified in an arbitrary basis. We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models. Importantly, our bounds make explicit distinction between parameters that are proper to the model and priors used as an input to the algorithm. Finally, we show that the interaction screening framework includes all models previously considered in the literature as special cases, and for which our analysis shows a systematic improvement in sample complexity.

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Novak, Thomas P. "MANOVAMAP: Graphical Representation of MANOVA in Marketing Research." Journal of Marketing Research 32, no. 3 (August 1995): 357–74. http://dx.doi.org/10.1177/002224379503200310.

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The author proposes a graphical representation of the multivariate analysis of variance (MANOVA). This representation, termed a “MANOVAMAP,” shows the magnitude of MANOVA model effects and their statistical significance in an easily interpreted statistical graphic. The author discusses the use and construction of MANOVAMAPs for an empirical example and compares it with both a traditional MANOVA analysis of the data and a traditional graphical analysis based on centroid plots.

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Gómez, Daniel. "Spectron: Graphical Model for Interacting With Timbre." TecnoLógicas, no. 22 (June 27, 2009): 61. http://dx.doi.org/10.22430/22565337.232.

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Los algoritmos para crear y manipular el sonido por medios electrónicos o digitales han crecido en cantidad y complejidad desde la creación de los primeros sintetizadores análogos. Sin embargo, las técnicas para visualizar estos modelos de síntesis no han crecido a la par de los sintetizadores hardware o software. En este artículo se muestran posibilidades para representar y controlar gráficamente el timbre, basadas en la visualización de los parámetros involucrados en su modelo de síntesis. Un grupo de datos muy simple fue extraído de un sintetizador substractivo comercial y analizado con dos aproximaciones diferentes, reducción dimensional y visualización abstracta de datos. Los resultados de estas aproximaciones diferentes fueron usados como lineamientos para crear un prototipo de sintetizador digital: el sintetizador Spectron. Este prototipo usa el gráfico de Amplitud vs. Frecuencia como su principal herramienta para informar a cerca del timbre e interactuar con el, fue desarrollado en PureData y su control plantea una simplificación en la cantidad de variables de un oscilador clásico al mismo tiempo que expande las posibilidades para generar timbres adicionales a los de estos osciladores clásicos.

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Park, Beomjin, Hosik Choi, and Changyi Park. "Negative binomial graphical model with excess zeros." Statistical Analysis and Data Mining: The ASA Data Science Journal 14, no. 5 (July 21, 2021): 449–65. http://dx.doi.org/10.1002/sam.11536.

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Ahn, Gil Seung, and Sun Hur. "Probabilistic Graphical Model for Transaction Data Analysis." Journal of Korean Institute of Industrial Engineers 42, no. 4 (August 15, 2016): 249–55. http://dx.doi.org/10.7232/jkiie.2016.42.4.249.

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