Bibliografías temáticas / Phylogeny Bayesian statistical decision theory

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Literatura académica sobre el tema "Phylogeny Bayesian statistical decision theory"

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Publicado: 4 de junio de 2021
Última modificación: 11 de febrero de 2022

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Artículos de revistas sobre el tema "Phylogeny Bayesian statistical decision theory"

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de la Horra, Julián. "Bayesian robustness of the quantile loss in statistical decision theory". Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas 107, n.º 2 (16 de mayo de 2012): 451–58. http://dx.doi.org/10.1007/s13398-012-0070-x.

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Procaccia, H., R. Cordier y S. Muller. "Application of Bayesian statistical decision theory for a maintenance optimization problem". Reliability Engineering & System Safety 55, n.º 2 (febrero de 1997): 143–49. http://dx.doi.org/10.1016/s0951-8320(96)00006-3.

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Reinhardt, Howard E. "Statistical Decision Theory and Bayesian Analysis. Second Edition (James O. Berger)". SIAM Review 29, n.º 3 (septiembre de 1987): 487–89. http://dx.doi.org/10.1137/1029095.

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Laedermann, Jean-Pascal, Jean-François Valley y François O. Bochud. "Measurement of radioactive samples: application of the Bayesian statistical decision theory". Metrologia 42, n.º 5 (13 de septiembre de 2005): 442–48. http://dx.doi.org/10.1088/0026-1394/42/5/015.

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Luce, Bryan R., Ya-Chen Tina Shih y Karl Claxton. "INTRODUCTION". International Journal of Technology Assessment in Health Care 17, n.º 1 (enero de 2001): 1–5. http://dx.doi.org/10.1017/s0266462301104010.

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Until the mid-1980s, most economic analyses of healthcare technologies were based on decision theory and used decision-analytic models. The goal was to synthesize all relevant clinical and economic evidence for the purpose of assisting decision makers to efficiently allocate society's scarce resources. This was true of virtually all the early cost-effectiveness evaluations sponsored and/or published by the U.S. Congressional Office of Technology Assessment (OTA) (15), Centers of Disease Control and Prevention (CDC), the National Cancer Institute, other elements of the U.S. Public Health Service, and of healthcare technology assessors in Europe and elsewhere around the world. Methodologists routinely espoused, or at minimum assumed, that these economic analyses were based on decision theory (8;24;25). Since decision theory is rooted in—in fact, an informal application of—Bayesian statistical theory, these analysts were conducting studies to assist healthcare decision making by appealing to a Bayesian rather than a classical, or frequentist, inference approach. But their efforts were not so labeled. Oddly, the statistical training of these decision analysts was invariably classical, not Bayesian. Many were not—and still are not—conversant with Bayesian statistical approaches.
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Geisler, Wilson S. y Randy L. Diehl. "Bayesian natural selection and the evolution of perceptual systems". Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, n.º 1420 (29 de abril de 2002): 419–48. http://dx.doi.org/10.1098/rstb.2001.1055.

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In recent years, there has been much interest in characterizing statistical properties of natural stimuli in order to better understand the design of perceptual systems. A fruitful approach has been to compare the processing of natural stimuli in real perceptual systems with that of ideal observers derived within the framework of Bayesian statistical decision theory. While this form of optimization theory has provided a deeper understanding of the information contained in natural stimuli as well as of the computational principles employed in perceptual systems, it does not directly consider the process of natural selection, which is ultimately responsible for design. Here we propose a formal framework for analysing how the statistics of natural stimuli and the process of natural selection interact to determine the design of perceptual systems. The framework consists of two complementary components. The first is a maximum fitness ideal observer, a standard Bayesian ideal observer with a utility function appropriate for natural selection. The second component is a formal version of natural selection based upon Bayesian statistical decision theory. Maximum fitness ideal observers and Bayesian natural selection are demonstrated in several examples. We suggest that the Bayesian approach is appropriate not only for the study of perceptual systems but also for the study of many other systems in biology.
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Galvani, Marta, Chiara Bardelli, Silvia Figini y Pietro Muliere. "A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap". Algorithms 14, n.º 1 (3 de enero de 2021): 11. http://dx.doi.org/10.3390/a14010011.

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Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional ϕ(F), where F is a random distribution function. Efron’s and Rubin’s bootstrap procedures can be extended, introducing an informative prior through the Proper Bayesian bootstrap. In this paper different bootstrap techniques are used and compared in predictive classification and regression models based on ensemble approaches, i.e., bagging models involving decision trees. Proper Bayesian bootstrap, proposed by Muliere and Secchi, is used to sample the posterior distribution over trees, introducing prior distributions on the covariates and the target variable. The results obtained are compared with respect to other competitive procedures employing different bootstrap techniques. The empirical analysis reports the results obtained on simulated and real data.
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Moore, Brian R., Sebastian Höhna, Michael R. May, Bruce Rannala y John P. Huelsenbeck. "Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures". Proceedings of the National Academy of Sciences 113, n.º 34 (10 de agosto de 2016): 9569–74. http://dx.doi.org/10.1073/pnas.1518659113.

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Bayesian analysis of macroevolutionary mixtures (BAMM) has recently taken the study of lineage diversification by storm. BAMM estimates the diversification-rate parameters (speciation and extinction) for every branch of a study phylogeny and infers the number and location of diversification-rate shifts across branches of a tree. Our evaluation of BAMM reveals two major theoretical errors: (i) the likelihood function (which estimates the model parameters from the data) is incorrect, and (ii) the compound Poisson process prior model (which describes the prior distribution of diversification-rate shifts across branches) is incoherent. Using simulation, we demonstrate that these theoretical issues cause statistical pathologies; posterior estimates of the number of diversification-rate shifts are strongly influenced by the assumed prior, and estimates of diversification-rate parameters are unreliable. Moreover, the inability to correctly compute the likelihood or to correctly specify the prior for rate-variable trees precludes the use of Bayesian approaches for testing hypotheses regarding the number and location of diversification-rate shifts using BAMM.
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Borysova, Valentyna I. y Bohdan P. Karnaukh. "Standard of proof in common law: Mathematical explication and probative value of statistical data". Journal of the National Academy of Legal Sciences of Ukraine 28, n.º 2 (25 de junio de 2021): 171–80. http://dx.doi.org/10.37635/jnalsu.28(2).2021.171-180.

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As a result of recent amendments to the procedural legislation of Ukraine, one may observe a tendency in judicial practice to differentiate the standards of proof depending on the type of litigation. Thus, in commercial litigation the so-called standard of “probability of evidence” applies, while in criminal proceedings – “beyond a reasonable doubt” standard applies. The purpose of this study was to find the rational justification for the differentiation of the standards of proof applied in civil (commercial) and criminal cases and to explain how the same fact is considered proven for the purposes of civil lawsuit and not proven for the purposes of criminal charge. The study is based on the methodology of Bayesian decision theory. The paper demonstrated how the principles of Bayesian decision theory can be applied to judicial fact-finding. According to Bayesian theory, the standard of proof applied depends on the ratio of the false positive error disutility to false negative error disutility. Since both types of error have the same disutility in a civil litigation, the threshold value of conviction is 50+ percent. In a criminal case, on the other hand, the disutility of false positive error considerably exceeds the disutility of the false negative one, and therefore the threshold value of conviction shall be much higher, amounting to 90 percent. Bayesian decision theory is premised on probabilistic assessments. And since the concept of probability has many meanings, the results of the application of Bayesian theory to judicial fact-finding can be interpreted in a variety of ways. When dealing with statistical evidence, it is crucial to distinguish between subjective and objective probability. Statistics indicate objective probability, while the standard of proof refers to subjective probability. Yet, in some cases, especially when statistical data is the only available evidence, the subjective probability may be roughly equivalent to the objective probability. In such cases, statistics cannot be ignored
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De Waal, D. J. "Summary on Bayes estimation and hypothesis testing". Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 7, n.º 1 (17 de marzo de 1988): 28–32. http://dx.doi.org/10.4102/satnt.v7i1.896.

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Although Bayes’ theorem was published in 1764, it is only recently that Bayesian procedures were used in practice in statistical analyses. Many developments have taken place and are still taking place in the areas of decision theory and group decision making. Two aspects, namely that of estimation and tests of hypotheses, will be looked into. This is the area of statistical inference mainly concerned with Mathematical Statistics.
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Tesis sobre el tema "Phylogeny Bayesian statistical decision theory"

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Liu, Liang. "Reconstructing posterior distributions of a species phylogeny using estimated gene tree distributions". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155754980.

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Lepage, Thomas. "The impact of variable evolutionary rates on phylogenetic inference : a Bayesian approach". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103264.

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In this dissertation, we explore the effect of variable evolutionary rates on phylogenetic inference. In the first half of the thesis are introduced the biological fundamentals and the statistical framework that will be used throughout the thesis. The basic concepts in phylogenetics and an overview of Bayesian inference are presented in Chapter 1. In Chapter 2, we survey the models that are already used for rate variation. We argue that the CIR process---a diffusion process widely used in finance---is the best suited for applications in phylogenetics, for both mathematical and computational reasons. Chapter 3 shows how evolutionary rate models are incorporated to DNA substitution models. We derive the general formulae for transition probabilities of substitutions when the rate is a continuous-time Markov chain, a diffusion process or a jump process (a diffusion process with discrete jumps).
The second half of the thesis is dedicated to applications of variable evolutionary rate models in two different contexts. In Chapter 4, we use the CIR process to model heterotachy, an evolutionary hypothesis according to which positions of an alignment may evolve at rates that vary with time differently from site to site. A comparison the CIR process with the covarion---a widely-used heterotachous model---on two different data sets allows us to conclude that the CIR provides a significantly better fit. Our approach, based on a Bayesian mixture model, enables us to determine the level of heterotachy at each site. Finally, the impact of variable evolutionary rates on divergence time estimation is explored in Chapter 5.
Several models, including the CIR process are compared on three data sets. We find that autocorrelated models (including the CIR) provide the best fits.
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Carvalho, Ricardo Durães de 1985. "Investigação e caracterização filogenética de Coronavírus na biota de aves silvestres e sinantrópicas provenientes das regiões Sul e Sudeste do Brasil". [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/316639.

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Orientadores: Clarice Weis Arns, Márcia Bianchi dos Santos
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia
Made available in DSpace on 2018-08-27T11:13:27Z (GMT). No. of bitstreams: 1 Carvalho_RicardoDuraesde_D.pdf: 3518273 bytes, checksum: 7b6f8b159eb057429823e23f6852c29b (MD5) Previous issue date: 2015
Resumo: A evolução e a dinâmica populacional dos Coronavírus (CoVs) ainda permanecem pouco exploradas. No presente estudo, análises filogenéticas e de filogeografia foram conduzidas para investigar a dinâmica evolutiva dos CoVs detectados em aves silvestres e sinantrópicas. Um total de 500 amostras, que inclui os suabes traqueais e cloacais coletados de 312 aves silvestres pertencentes a 42 espécies, foram analisadas através da RT-qPCR. Sessenta e cinco amostras (13%) provenientes de 23 espécies foram positivas para o Coronavírus aviário (AvCoV). Trezentos e duas amostras foram investigadas para a pesquisa do Pan-Coronavírus (Pan-CoV) através do nPCR, destas, 17 (5,6%) foram positivas, sendo que 11 foram detectadas em espécies diferentes. Análises filogenéticas dos AvCoVs revelaram que as sequências de DNA das amostras coletadas no Brasil não agruparam com nenhuma das sequências do gene Spike (S1) dos AvCoVs depositados no banco de dados GenBank. Análise Bayesiana estimou uma variante do AvCoV proveniente da Suécia (1999) como o ancestral comum mais recente dos AvCoVs detectados neste estudo. Além disso, as análises realizadas através do "Bayesian Skyline Plot" (BSP) inferiram um aumento na dinâmica da população demográfica do AvCoV em diferentes espécies de aves silvestres e sinantrópicas. As análises filogenéticas do Pan-CoV mostrou que a maioria das amostras se agruparam com o Vírus da Hepatite Murina A59 (MHV A59), CoV pertencente ao grupo dos Beta-CoVs. Uma amostra [CoV detectado em Amazona vinacea(Papagaio-de-peito-roxo)] se agrupou com um CoV de Suínos, o PCoV HKU15, que pertence ao gênero Delta-CoV, ainda não relatado na América do Sul. Nossos achados sugerem que as aves podem ser novos potenciais hospedeiros responsáveis pela propagação e disseminação de diferentes CoVs para diferentes espécies de animais
Abstract: The evolution and population dynamics of Coronaviruses (CoVs) still remain underexplored. In the present study, phylogenetic and phylogeographic analyseswere conducted to investigate the evolutionary dynamics of CoV detected in wild and synanthropic birds. A total of 500 samples, including tracheal and cloacal swabs collected from 312 wild birds belonging to 42species, were analysed by RT-qPCR. A total of 65 samples from 23bird species were positive for Avian Coronaviruses (AvCoVs).Three hundred and two samples were screened for the Pan-Coronavirus (Pan-CoV) through the nPCR, 17 (5.6%) were positive, being that 11 were detected in different species. AvCoVs phylogenetic analyses revealed that the DNA sequences from samples collected in Brazil did not cluster with any of the AvCoV S1 gene sequences deposited in the GenBank database. Bayesian framework analysis estimated an AvCoV strain from Sweden (1999) as the most recent common ancestor of the AvCoVs detected in this study. Furthermore, Bayesian Skyline Plot (BSP) analysis inferred an increase in the AvCoV dynamic demographic population in different wild and synanthropic bird species. Phylogenetic analysis of the Pan-CoV showed that most of the samples clustered with the Murine Hepatitis Virus A59 strain (MHV A59) belong to the BetaCoV group. Besides, one of our samples [CoV detected in Amazona vinacea (parrot-breasted-purple)] clustered with a CoV isolated from pigs, PCoV HKU15, belonging to the DeltaCoV genus, still not reported in South America. Our findings suggest that birds may be potential new hosts responsible for spreading of different CoVs for different species of animals
Doutorado
Microbiologia
Doutor em Genetica e Biologia Molecular
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Cheng, Dunlei Stamey James D. "Topics in Bayesian sample size determination and Bayesian model selection". Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5039.

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Luo, Wuben. "A comparative assessment of Dempster-Shafer and Bayesian belief in civil engineering applications". Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28500.

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The Bayesian theory has long been the predominate method in dealing with uncertainties in civil engineering practice including water resources engineering. However, it imposes unnecessary restrictive requirements on inferential problems. Concerns thus arise about the effectiveness of using Bayesian theory in dealing with more general inferential problems. The recently developed Dempster-Shafer theory appears to be able to surmount the limitations of Bayesian theory. The new theory was originally proposed as a pure mathematical theory. A reasonable amount of work has been done in trying to adopt this new theory in practice, most of this work being related to inexact inference in expert systems and all of the work still remaining in the fundamental stage. The purpose of this research is first to compare the two theories and second to try to apply Dempster-Shafer theory in solving real problems in water resources engineering. In comparing Bayesian and Dempster-Shafer theory, the equivalent situation between these two theories under a special situation is discussed first. The divergence of results from Dempster-Shafer and Bayesian approaches under more general situations where Bayesian theory is unsatisfactory is then examined. Following this, the conceptual difference between the two theories is argued. Also discussed in the first part of this research is the issue of dealing with evidence including classifying sources of evidence and expressing them through belief functions. In attempting to adopt Dempster-Shafer theory in engineering practice, the Dempster-Shafer decision theory, i.e. the application of Dempster-Shafer theory within the framework of conventional decision theory, is introduced. The application of this new decision theory is demonstrated through a water resources engineering design example.
Applied Science, Faculty of
Civil Engineering, Department of
Graduate
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Yeo, Yeongseo. "Bayesian scientific methodology : a naturalistic approach /". free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3074459.

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Pei, Xin y 裴欣. "Bayesian approach to road safety analyses". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46591989.

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Ma, Yimin. "Bayesian and empirical Bayesian analysis for the truncation parameter distribution families /". *McMaster only, 1998.

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Feng, Chunyao Seaman John Weldon. "Bayesian evaluation of surrogate endpoints". Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/4187.

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Keim, Michelle. "Bayesian information retrieval /". Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8937.

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Libros sobre el tema "Phylogeny Bayesian statistical decision theory"

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Bayesian phylogenetics: Methods, algorithms, and applications. Boca Raton: CRC Press/Taylor & Francis, 2014.

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Statistical decision theory and Bayesian analysis. 2a ed. New York: Springer-Verlag, 1993.

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Berger, James O. Statistical decision theory and Bayesian analysis. 2a ed. New York: Springer-Verlag, 1985.

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Berger, James O. Statistical Decision Theory and Bayesian Analysis. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4757-4286-2.

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O, Berger James, ed. Statistical decision theory and Bayesian analysis. 2a ed. New York: Springer-Verlag, 1985.

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Bayesian statistical modelling. 2a ed. Chichester, England: John Wiley & Sons, 2006.

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Decision analysis: A Bayesian approach. London: Chapman and Hall, 1988.

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1934-, Madansky Albert y McCulloch Robert E, eds. Elementary Bayesian statistics. Cheltenham, UK: Edward Elgar, 1997.

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Inc, ebrary, ed. Bayesian econometrics. Bingley: Emerald JAI, 2008.

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Guttman, Irwin. Bayesian power. Toronto: University of Toronto, Dept. of Statistics, 1986.

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Capítulos de libros sobre el tema "Phylogeny Bayesian statistical decision theory"

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Longford, Nicholas T. "The Bayesian Paradigm". En Statistical Decision Theory, 49–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40433-7_4.

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Kachiashvili, K. J. "Constrained Bayesian Rules for Testing Statistical Hypotheses". En Strategic Management, Decision Theory, and Decision Science, 159–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1368-5_11.

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Diaconis, Persi. "Bayesian Numerical Analysis". En Statistical Decision Theory and Related Topics IV, 163–75. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8768-8_20.

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Zellner, Arnold. "Bayesian and Non-Bayesian Estimation Using Balanced Loss Functions". En Statistical Decision Theory and Related Topics V, 377–90. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_28.

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Bernardo, José M. "Bayesian Linear Probabilistic Classification". En Statistical Decision Theory and Related Topics IV, 151–62. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8768-8_19.

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Berger, J. O., B. Boukai y Y. Wang. "Properties of Unified Bayesian-Frequentist Tests". En Advances in Statistical Decision Theory and Applications, 207–23. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-2308-5_14.

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Bernardo, José M. "Bayesian Estimation of Political Transition Matrices". En Statistical Decision Theory and Related Topics V, 135–40. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_11.

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Whitehead, John. "Using Bayesian Decision Theory in Dose-Escalation Studies". En Statistical Methods for Dose-Finding Experiments, 149–71. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470861258.ch7.

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Ghosh, Malay. "On Some Bayesian Solutions of the Neyman-Scott Problem". En Statistical Decision Theory and Related Topics V, 267–76. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_20.

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Liang, TaChen. "On Hierarchical Bayesian Estimation and Selection for Multivariate Hypergeometric Distributions". En Advances in Statistical Decision Theory and Applications, 49–64. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-2308-5_4.

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Actas de conferencias sobre el tema "Phylogeny Bayesian statistical decision theory"

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Kim, Taewung y Hyun-Yong Jeong. "A Crash Prediction Algorithm Using a Particle Filter and Bayesian Decision Theory". En ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12118.

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Active safety systems have been developed in automotive industry, and a tracking algorithm and a threat assessment algorithm are needed in such systems to predict the collision between vehicles. It is difficult to track a threat vehicle accurately because of lack of information on a threat vehicle and the measurement noise which does normally not follow Gaussian distribution. Therefore, there is an uncertainty whether the collision will occur or not. Particle filtering is widely used for nonlinear and non-Gaussian tracking problems, and statistical decision theory can be used to make an optimal decision in an uncertain case. In this study, a crash prediction algorithm has been developed using a particle filter and statistical decision making.
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Ciabarri, Fabio, Marco Pirrone y Cristiano Tarchiani. "ANALYTICAL UNCERTAINTY PROPAGATION IN FACIES CLASSIFICATION WITH UNCERTAIN LOG-DATA". En 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0071.

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Log-facies classification aims to predict a vertical profile of facies at well location with log readings or rock properties calculated in the formation evaluation and/or rock-physics modeling analysis as input. Various classification approaches are described in the literature and new ones continue to appear based on emerging Machine Learning techniques. However, most of the available classification methods assume that the inputs are accurate and their inherent uncertainty, related to measurement errors and interpretation steps, is usually neglected. Accounting for facies uncertainty is not a mere exercise in style, rather it is fundamental for the purpose of understanding the reliability of the classification results, and it also represents a critical information for 3D reservoir modeling and/or seismic characterization processes. This is particularly true in wells characterized by high vertical heterogeneity of rock properties or thinly bedded stratigraphy. Among classification methods, probabilistic classifiers, which relies on the principle of Bayes decision theory, offer an intuitive way to model and propagate measurements/rock properties uncertainty into the classification process. In this work, the Bayesian classifier is enhanced such that the most likely classification of facies is expressed by maximizing the integral product between three probability functions. The latters describe: (1) the a-priori information on facies proportion (2) the likelihood of a set of measurements/rock properties to belong to a certain facies-class and (3) the uncertainty of the inputs to the classifier (log data or rock properties derived from them). Reliability of the classification outcome is therefore improved by accounting for both the global uncertainty, related to facies classes overlap in the classification model, and the depth-dependent uncertainty related to log data. As derived in this work, the most interesting feature of the proposed formulation, although generally valid for any type of probability functions, is that it can be analytically solved by representing the input distributions as a Gaussian mixture model and their related uncertainty as an additive white Gaussian noise. This gives a robust, straightforward and fast approach that can be effortlessly integrated in existing classification workflows. The proposed classifier is tested in various well-log characterization studies on clastic depositional environments where Monte-Carlo realizations of rock properties curves, output of a statistical formation evaluation analysis, are used to infer rock properties distributions. Uncertainty on rock properties, modeled as an additive white Gaussian noise, are then statistically estimated (independently at each depth along the well profile) from the ensemble of Monte-Carlo realizations. At the same time, a classifier, based on a Gaussian mixture model, is parametrically inferred from the pointwise mean of the Monte Carlo realizations given an a-priori reference profile of facies. Classification results, given by the a-posteriori facies proportion and the maximum a-posteriori prediction profiles, are finally computed. The classification outcomes clearly highlight that neglecting uncertainty leads to an erroneous final interpretation, especially at the transition zone between different facies. As mentioned, this become particularly remarkable in complex environments and highly heterogeneous scenarios.
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