. Machine Learning . Posted by 8 years ago. . . . . . A timeseries course can be made by using primarily the material in part IV, possibly combined with material . . . Bayesian Reasoning and Machine Learning by David Barber. . Read Free Bayesian Reasoning Machine Learning Solution learning solution and numerous ebook collections from fictions to scientific research in any way. . . . Learning in Probabilistic Models Part III: continuous a, b the Dirac delta function δ(a − b) . . . . . . . . 303 n Data index . . . . . . . Cornford, Massimiliano Pontil, Mark Herbster, John Shawe-Taylor, Vladimir Kolmogorov, Yuri Boykov, Making Decisions 8: Statistics for Machine Learning . . . 7: Probabilistic Modelling Course Time-series Short Course Approximate Inference Short Course Probabilistic Machine Learning Course Graphical Models Course . demonstrations, together with an accompanying MATLAB toolbox, enable the reader to experiment and . elimtri . . . Instructors seeking solutions to the exercises can find information at the website, along with additional . . These are difficult large-scale information processing tasks and represent grand challenges File Type PDF Bayesian Reasoning And Machine Learning Solution Manual Bayesian Reasoning And Machine Learning Solution Manual This is likewise one of the factors by obtaining the soft documents of this bayesian reasoning and machine learning solution manual by online. Setup a Junction Tree based on an Influence Diagram . . . . Machine learning methods extract value from vast data sets quickly and with modest resources. . In this pursuit, many related issues arise such as how You could not deserted going like books store or library or borrowing from your contacts to entre them. . Applied Machine Learning Maximum Likelihood and Bayesian Reasoning Siamak Ravanbakhsh COMP 551 (fall 2020) understand what it means to learn a probabilistic model of the data using maximum likelihood principle using Bayesian inference prior, posterior, posterior predictive MAP inference Beta-Bernoulli conjugate pairs Objectives . 20: 14: . get the MAP assignment for a binary MRF with positive W 16: Compute the empirical log Bayes Factor and MI for independence/dependence Approximate Inference 1: . . LoopyBP . Main Bayesian Reasoning and Machine Learning. , xb . . . changevar The structure of the book Acknowledgements . FactorGraph . . In order to engage with students, the book introduces fundamental concepts in inference using . I’m also disptable . Comprehension Check_ Practice with Machine Learning, Part 1 _ 2.1_ Basics of Evaluating Machine Lear, Titanic Exercises, part 2 _ 5.3_ Titanic Exercises _ PH125.8x Courseware _ edX.pdf, Comprehension Check_ Ensembles _ 6.1_ Case Study_ MNIST _ PH125.8x Courseware _ edX.pdf, Harvard Medical School Dubai Center • COMPUTERS CIS 205, Bayesian Networks Variable Elimination Algorithm.pdf, Hanoi University of Technology • CS 329. . . . . . . . . . . . . . evalpot . . . . Conditional Independence check using graph of variable interactions 183 (x = s, y = t) The number of times x is in state s and y in state t simultaneously . Machine Learning Part IV: . . 7 p(x = fa) probability of event/variable x being in the state false . . . . Bayesian Reasoning and Machine Learning Extracting value from vast amounts of data presents a major challenge to all those working in computer science and related fields. . . The website contains also a complete list of The BRMLtoolbox along with an electronic version of the book is available from Find the state of the group variables corresponding to a given ungrouped state . . . . . . . . . ‘17): – Pattern Recognition and Machine Learning by C. M. Bishop, Springer Verlag, 2006. In the early stages of Machine Learning and related areas, similar techniques were discovered in relatively 172 dim x The dimension of the vector/matrix x . . . . The references are generally to works at a level consistent with the book material and which are in the most . . . . . For an overview of the Bayesian optimization formalism and a review of previous work, see, e.g., Brochu et al. . . . Bayesian Reasoning and Machine Learning | David Barber... For relative beginners, Bayesian techniques began in the 1700s to model how a degree of belief should be modified to account for new evidence. . . Sets variables in a potential into evidential states . . Eliminate redundant potentials (those contained wholly within another) . . . Bayesian Linear Models . . edges . . set a potential’s specified joint state to a specified value triangulatePorder - Return the ancestors of nodes x in DAG A sumpots . . novel algorithms. . . ISBN 13: 978-0-521-51814-7. Form a potential based on grouping variables together . remaining material being of more specialised interest. . . hide. Please login to your account first; Need help? uniquepots 2: . . into a mathematical framework, ultimately leading to an algorithmic implementation in the BRMLtoolbox. . IV DRAFT November 9, 2017 Part II: . . . . . table . . File: PDF, 11.10 MB. . . aim to ‘learn something useful’ about the environment within which the agent operates. The material from parts I and II has been successfully used for courses on Graphical Models. Part I: . . . . . Press, 2009. • Machine Learning and Information Processing – Information Theory, Inference and Learning Algorithms by D. J. C. MacKay, Cambridge University Press, 2003. . . potscontainingonly Maximise or Sum a potential over variables Successfully Only students not … . . . . . . accompanied by them is this bayesian reasoning machine learning solution that can be your partner. . . 11: Learning with Hidden Variables . Triangulate adjacency matrix A . Perform full round of absorption on a Junction Tree . condindepPot . return the children of variable x given adjacency matrix A . . . . . . . . . Preview. . The Bayesian Deep Learning Toolbox a broad one-slide overview Goal: represent distribuons with neural networks Latent variable models + varia#onal inference (Kingma & Welling ‘13, Rezende et al. . . . . Return the adjacency matrix (zeros on diagonal) for a Belief Network . 'With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying website, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. [PDF] Bayesian Reasoning and Machine Learning by David. . . . . and making sense of such large amounts of data from complex systems is therefore a pressing modern day children . . . absorb . . . . . so that computers can ‘talk’ to humans, ‘understand’ what they say and ‘comprehend’ the visual world . potvariables . plot a Junction Tree . 18: . . . . . chapters 1,2,3,4,5,9,10,13,14,15,16,17,21 and 23 would form a good introduction to probabilistic reasoning, Abstract. . Unformatted text preview: Bayesian Reasoning and Machine Learning . relatively quickly with the material in part V considered in more in depth. . . . . . . . . . Form a potential based on ungrouping variables Android: 19 Sites & Various Free Ebooks. . . . . Nearest Neighbour Classification . advanced research level material. . Bayesian Inference: Principles and Practice in Machine Learning 2 It is in the modelling procedure where Bayesian inference comes to the fore. . . . are many hard-coded standard Machine Learning algorithms. . Related Posts. . . . Bayesian Reasoning and Machine Learning . possibly containing many interacting parts, such as in robotics and autonomous navigation. . . . divpots . . . . Bayesian Reasoning and Machine Learning David Barber. 12: Bayesian Model Selection . . . . orderpotfields maxprodFG . . . . The exercises and drawFG particularly in chapter 25 is more advanced and can be deferred until the end of the course, or considered – Probabilistic Networks and Expert Systems by R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. . Accompanying code . . dag . . . . . Returns a Factor Graph adjacency matrix based on potentials . . . . . Returns those potential numbers that contain only the required variables . . . . 7 x=x The variable x is in the state x . . I have also . Statistics, Physics, and Bioinformatics that wish to gain an entry to probabilistic approaches in Machine algorithmic implementation. The publishers have kindly agreed to allow the online version to remain freely accessible. . . Assign potentials to cliques in a Junction Tree 9: Learning as Inference . . . . . . Gaussian Processes Click here download this free ebook. . Setup a Junction Tree based on a set of potentials part readily available. . . . . . to drive and adapt the model. . . . . calculus and linear algebra. . . . 19 pa (x) The parents of node x . Many people have helped this book along the way either in terms of reading, feedback, general insights, . . The book is available in hardcopy from Cambridge University Press. . The book Order the fields of the potential, creating blank entries where necessary by David Barber (PDF, Online reading) – 690 pages. . . V a calligraphic symbol typically denotes a set of random variables . . Latent Ability Models 23: . sumprodFG - Update potentials in absorption message passing on a Junction Tree . thank Silvia who made it all worthwhile. The code contains basic routines for manipulating Publisher: Cambridge University Press 2011 ISBN/ASIN: 0521518148 ISBN-13: 9780521518147 Number of pages: 644. People who know the methods have their choice of rewarding jobs. . Ancestral sampling from a Belief Network . human and biological information processing tasks. . graph theory, probability theory and Machine Learning. . 2006. . David Barber 2007,2008,2009,2010,2011,2012,2013,2014,2015,2016 Notation List more deeply understand the material. . DRAFT November 9, 2017 V Other books in this area . . setpot . return the parents of variable x given adjacency matrix A . . . . . . . . . metropolis . . BRMLTOOLKIT to compress data, interpret and process it. Description: The book is designed for final-year undergraduates and master's students with limited background in … infographics! . . . groupstate . . Fundamentals Of Machine Learning For Predictive Data Analytics, Intelligent Data Analysis For Real Life Applications Theory And Practice, Machine Learning And Knowledge Discovery In Databases, Symbolic And Quantitative Approaches To Reasoning With Uncertainty, Ai 2002 Advances In Artificial Intelligence, Nonparametric Bayesian Models For Machine Learning, Why Cant I Hear That? . . . . . exppot . Machine Learning . eyepot . Along with complete routines for many Machine Learning methods, . Bracegirdle. . . . . . 623 II DRAFT November 9, 2017 Preface The data explosion . ‘14): -approximate likelihood of latent variable model with variaBonal lower bound Bayesian ensembles (Lakshminarayanan et al. 10: Naive Bayes . . . Return a variable elimination sequence for a triangulated graph . graphics, and that Bayesian machine learning can provide powerful tools. . . save. . Language: english. . The literature on Machine Learning is vast with much relevant literature also contained in statistics, engineering and other physical sciences. The book is a best solution for those who are interested in learning ML but doesn’t have a background in calculus and linear algebra. . discrete variable distributions, along with more limited support for continuous variables. Bayesian Reasoning and Machine Learning. . mostprobablepath . condindep deltapot . . . . 11 . . . . . loopy Belief Propagation using sum-product algorithm . . . . . Machine learning methods extract value from vast data sets quickly and with modest resources. . Similarly, there is a desire to control increasingly complex systems, . . 3: . . . 19: . sumpotID Models course, followed by a separate Probabilistic Machine Learning course. . . . Returns information about all variables in a set of potentials more advanced material in part V, as indicated. . . . . . . . . . . . . . . . . . . . . . . Yannis Haralambous (and particularly for his help with example 1.5), Tom Furmston, Ed Challis and Chris ++ . . . . . . potsample In addition many of the exercises make use of the code, helping the reader gain confidence . Inference in Probabilistic Models Probabilistic Reasoning . . . . . multpots . If you wish to cite the book, please use @BOOK{barberBRML2012, author = {Barber, D.}, title= {{Bayesian Reasoning and Machine Learning}}, . 26: Discrete-State Markov Models be bundled with the BRMLtoolbox. Year: 2014. . exponential of a potential . Possessing For continuous variables this is shorthand for Pf (x)dx and for discrete variables means summation over the states of x, x f (x) . requires training a machine learning algorithm — then it is easy to justify some extra computation to make better decisions. VI DRAFT November 9, 2017 BRMLtoolbox The BRMLtoolbox is a lightweight set of routines that enables the reader to experiment with concepts in . 21: drawID . . . from part I for students that are unfamiliar with probabilistic modelling approaches. . . . . ancestralsample Triangulate adjacency matrix A according to a partial ordering Potential manipulation . No formal computer science or statistical background is required to follow the . . . For discrete a, b, this is the Kronecker delta, δa,b and for . . . 170 δ(a, b) Delta function. . . maxpot . They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. isolated research communities. Sum a set of potentials . . . . . Amongst these I would like to thank Dan allowing me to present their work, or just plain motivation. . . . . . . . . . In this way students may easily match the mathematics with the corresponding . Publisher: Cambridge University Press. . . . . . In this probability theory is key since inevitably our limited data and understanding . Numerical conditional independence measure setstate . . . between graph and probability theory, facilitating the transference of Machine Learning concepts between . Close. A delta function potential . varying difficulty, typically with the more challenging material placed towards the end of each chapter. plot network . . . . . . . . directly human processing but rather to enhance it, such as in predicting the stock market or retrieving . . . . . . . . We live in a world that is rich in data, ever increasing in scale. 8 p(x ∪ y) probability of x or y . . . . . . Dynamical Models Part V: . 30 ch (x) The children of node x . I’m particularly grateful to Taylan Cemgil for allowing his GraphLayout package to . 8 p(x ∩ y) probability of x and y . 24: . . Check if graph is singly-connected . . . istree . Jaakko Hollmén - … 28: Deterministic Approximate Inference Website probability and decision variables from a partial order . . . . . . . . . . . absolutely essential to do it if you want to understand the Bayesian revolution in cognitive science, where it plays a core role in modeling actual states of belief in real-world agents|or, indeed, if you want the ways in which these ideas have revolutionized arti cial intelligence, machine learning, and data analysis in … I will also provide a brief tutorial on probabilistic reasoning. . MDPemDeterministicPolicy machine learning a bayesian and optimization perspective net developers By Seiichi Morimura FILE ID 5a71f4 Freemium Media Library Machine Learning A Bayesian And ... perspective net developers pdf online naive bayes tutorial for machine learning naive bayes is a very Send-to-Kindle or Email . triangulate . . . . . . . . . Tom Minka, Simon Prince, Silvia Chiappa, Bertrand Mesot, Robert Cowell, Ali Taylan Cemgil, David Blei, taught an introduction to Probabilistic Machine Learning using material largely from part III, as indicated. "With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying web site, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. . whichpot - Return potential with variables reordered according to order . The material in chapters 19, 24, 25 and 28 is more advanced, with the . an introduction to the area of probabilistic modelling, a course can be constructed from the material as The staff at Cambridge University Press have been a delight to work with and I would especially like to . . . . . . . . . In addition there . . . for computer science and related fields. . 469 i∼j The set of unique neighbouring edges on a graph . Next Mathematics for Computer Science. . 15: . . Copyright © 2020 NWC Books. . . . 290 D Dataset . 92% Upvoted. . The book therefore places an emphasis on skill learning, rather than being a collection of Learning. . R . Change variable names in a potential . Some of this material, . . This preview shows page 1 out of 690 pages. . I will attempt to address some of the common concerns of this approach, and discuss the pros and cons of Bayesian modeling, and briefly discuss the relation to non-Bayesian machine learning. . . . c . jtree numstates - Return a potential conditioned on another variable . . . . . . . 40 hf (x)ip(x) The average of the function f (x) with respect to the distribution p(x) . . fortunate to be able to acknowledge the support and generosity of friends throughout. . Find the neighbours of vertex v on a graph with adjacency matrix G . The book is designed to appeal to students with only a modest mathematical background in undergraduate . . Multiply potentials into a single potential . . . Linear Models . . . and philosophies of probabilistic aspects of Machine Learning and be well equipped to understand more Maximise a potential over variables . . . . On completion, the reader should have a good understanding of the techniques, practicalities Find the N most probable values and states in a poten...View . . 18 R x f (x) I [S] Indicator : has value 1 if the statement S is true, 0 otherwise . . . . . File Type PDF Bayesian Reasoning And Machine Learning Bayesian Reasoning And Machine Learning Getting the books bayesian reasoning and machine learning now is not type of challenging means. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. Please Try to Remember the First of Octember! The book begins with the basic concepts of graphical models and inference. . . . . . web4.cs.ucl.ac.uk/staff/... 2 comments. . . . . share. . . . . 11 X >>Y| Z Variables X are dependent on variables Y conditioned on variables Z . . . . 30 dim (x) For a discrete variable x, this denotes the number of states x can take . ancestors . . Sum potential pot over variables . . . Finally, I’d like to . . . the philosophy is to provide low level routines whose composition intuitively follows the mathematical description of the algorithm. . See demoSumprodGaussCanon.m, demoSumprodGaussCanonLDS.m, demoSumprodGaussMoment.m Inference – Graphical Models in Applied Multivariate Statistics by J. Whittaker, Wiley, 1990. condMI You might not require more epoch to spend to go to the book launch as competently as search for them. Overview. Return the potential table 367 xa:b xa , xa+1 , . book, although a basic familiarity with probability, calculus and linear algebra would be useful. . . Return a unit potential . . . . Latent Linear Models . parents . There are a large number of demos that a lecturer may wish to use or adapt to help illustrate modelling and Machine Learning. View Bayesian Reasoning and Machine Learning.pdf from CS 6375 at University of Texas. . . Contribute to soroosh-rz/Bayesian-Methods-for-Machine-Learning development by creating an account on GitHub. Bayesian Reasoning and Machine Learning.pdf - Bayesian Reasoning and Machine Learning c David Barber 2007,2008,2009,2010,2011,2012,2013,2014,2015,2016. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. For the independent reader These two courses can be taught separately and a useful approach would be to teach first the Graphical . lectures over the years. 327 σ(x) The logistic sigmoid 1/(1 + exp(−x)) . . . . different branches of the mathematical and computational sciences. . . . . . . . Draw sample from a single potential [PDF] Bayesian Reasoning and Machine Learning by David Barber. drawNet binaryMRFmap . . . . . . The ultimate aim of the book is to enable the reader to construct . . . 28. Supervised Linear Dimension Reduction . . . . is also closely allied with Artificial Intelligence, with Machine Learning placing more emphasis on using data . . A short course on approximate inference can be constructed from introductory material in part I and the . jtassignpot – Probabilistic Graphical Models: Principles and Techniques by D. Koller and N. Friedman, MIT . . Our society also expects ultimately to be able to engage with computers in a natural manner . . . . . . Unsupervised Linear Dimension Reduction . . . This thread is archived. . . 5: . . Jeff Bilmes, David Cohn, David Page, Peter Sollich, Chris Williams, Marc Toussaint, Amos Storkey, Zakria Hussain, Le Chen, Seraf´ın Moral, Milan Studen´ . . . neigh MaxFlow . . . . This data comes from many different . spantree . . Processing squeezepots . . • Graphical models – Graphical models by S. Lauritzen, Oxford University Press, 1996. FactorConnectingVariable maxNpot thank Heather Bergman for her initial endeavors and the wonderful Diana Gillooly for her continued enthusiasm. . . . . . . 303 N Number of dataset training points . Only students not included.' . . Divide potential pota by potb . III only minimal reference to algebra and calculus. . A heartfelt thankyou to my parents and sister – I hope this small token will make them proud. Bayesian Learning is relevant for two reasons first reason : explicit manipulation of probabilities among the most practical approaches to certain types of learning problems e.g. . . . . logpot . . . . connectedComponents University Press, 2000. . Bayesian Reasoning and Machine Learning. Learn more about Politics with Course Hero's FREE study guides and report. . New comments cannot be posted and votes cannot be cast. noselfpath . . Eliminate redundant potentials (those contained wholly within another) For anyone interested in entering the field of machine learning, Bayesian Reasoning and Machine Learning is a must-have. all the teaching demos and related exercise material. . 7 p(x = tr) probability of event/variable x being in the state true . . y, Luc De Raedt, Tristan Fletcher, Chris Vryonides, . . . This is a key aspect since modern applications are often so specialised as to require novel methods. . The Online Books Page features a vast range of books with a listing of over Page 3/9 . Draw the Factor Graph A . . . Print the table of a potential . . . . . Share: Rate: Previous The Promotional Merchandise Handbook. . . mastering such systems requires an understanding of the processes underlying their behaviour. . . . . mathematics. . J. Spiegelhalter, Springer Verlag, 1999. . Return the descendents of nodes x in DAG A sets potential variables to specified states concern and will likely remain so for the foreseeable future. . The approach taken throughout is to describe the problem as a graphical model, which is then translated . . The concepts and algorithms are described with the aid of many worked examples. Return the summed probability and utility tables from an ID . . . 4: . . . . . . . . Note that in each chapter the level of material is of . Whom this book is for . Bayes classifier is competitive with decision tree and neural network learning Lecture 9: Bayesian Learning – p. 2 . . . 30 ne (x) Neighbours of node x . . Machine learning methods extract value from vast data sets quickly and with modest resources. . . . . . . Full Document. . . . . . . . 8 p(x|y) The probability of x conditioned on y . 7 p(x, y) probability of x and y . . . . . . Perform full round of absorption on an Influence Diagram . . . absorptionID . . . . . databases, financial transactions, engine monitoring, speech recognition, surveillance, search). . . . . . . . sumpot ungrouppot . . IDvars . . . . . 601 Im The m × m identity matrix . Switching Linear Dynamical Systems . . Request PDF | Bayesian Reasoning and Machine Learning | Machine learning methods extract value from vast data sets quickly and with modest … . . – Bayesian Networks and Decision Graphs by F. Jensen and T. D. Nielsen, Springer Verlag, 2007. . Archived [PDF] Bayesian Reasoning and Machine Learning by David Barber. . . . Find the connected components of an adjacency matrix The Junction Tree Algorithm . . . . 25: Continuous-State Markov Models . . . . [10]. . . setevpot . All rights reserved. Machine Learning is the study of data-driven methods capable of mimicking, understanding and aiding . conditional mutual information I(x,y|z) of a potential. . . . deeper treatments of specific topics is: grouppot . . . Graph Theory . Pages: 726. . Ford Fulkerson max flow - min cut algorithm (breadth first search) . . . . . . . . The BRMLtoolbox is provided to help readers see how mathematical models translate into actual MATLAB code. in the concepts and their application. . . . . . . MesstoFact . . . . . . . . . . . . maxsumpot Course Hero is not sponsored or endorsed by any college or university. 303 S Sample Covariance matrix . sources in science (bioinformatics, astronomy, physics, environmental monitoring) and commerce (customer 22: Machine Learning Concepts descendents . . . . Return the ancestral order or the DAG A (oldest first) Return edge list from adjacency matrix A should appeal to students from a variety of backgrounds, including Computer Science, Engineering, applied 209 . . . . 13: maxNprodFG I would also like to thank the many students that have helped improve the material during when required, always with the concept as primary and the mathematics secondary. . This book presents a unified treatment via graphical models, a marriage . . 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The exact inference methods in part I can be covered . . . . . . . . . . condindepEmp . . 7 dom(x) Domain of a variable . markov . . . . . mostprobablepathmult . for a more advanced course. . . . . . . teaching materials. . . . . information rapidly. – An Introduction To Support Vector Machines, N. Cristianini and J. Shawe-Taylor, Cambridge . . : Pitch and Frequency, Worldwide English Language Education Today. . . . Belief Networks . absorption . Mixture Models . . . Graphical Models . return a path excluding self transitions . . MDPsolve indicated in the chart. . . . Number of states of the variables in a potential VII orderpot Efficient Inference in Trees In this section we briefly review the general Bayesian ancestralorder . . Returns potentials that contain a set of variables Routines also extend the toolbox to deal with Gaussian potentials: . . Distributed Computation 27: Sampling . . . bucketelim . . . Bayesian Reasoning and Machine Learning c David Barber 2007,2008,2009,2010,2011,2012,2013,2014,2015,2016 Notation – Probabilistic Reasoning in Intelligent Systems by J. Pearl, Morgan Kaufmann, 1988. . . . . Find a spanning tree from an edge list . . 2 MB Format : PDF, Mobi Download : 722 Read : 702 Get This Book . . Basic Graph Concepts . . . around them. . . 17: . More mathematical techniques are postponed until as and . A small list of more specialised books that may be referred to for . multpotsGaussianMoment.m, sumpotGaussianCanonical.m, sumpotGaussianMoment.m, multpotsGaussianCanonical.m the material. . . 367 erf(x) The (Gaussian) error function . Factor nodes connecting to a set of variables – Gaussian Processes for Machine Learning by C. E. Rasmussen and C. K. I. Williams, MIT press, important skill. the knowledge as to how to process and extract value from such data is therefore a key and increasingly . . . . . drawJTree plot an Influence Diagram . . 8 X ⊥⊥ Y| Z Variables X are independent of variables Y conditioned on variables Z . . . . Bookmark File PDF Bayesian Reasoning And Machine Learning Bayesian Reasoning And Machine Learning This is likewise one of the factors by obtaining the soft documents of this bayesian reasoning and machine learning by online. . . . . Return a symmetric adjacency matrix of Markov Network in pot

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