Probabilistic Modeling in Machine Learning and Artificial Intelligence

Authors(3) :-Sajal Kaushik, Pulkit Kogat, Dr. Narina Thakur

Probabilistic modeling plays a vital role in inferencing from huge datasets with high probability of uncertainity. This paper introduces most common probabilistic models applicable in machine learning found in litreature. An extensive litreature survey on Bayesian Networks, Markov Models,Hidden markov Models and stochastic grammars is captured under this single formalism. It also discusses a generic formalism called Bayesian Programming. The following paper presents various probabilistic modeling techniques used in practical applications of machine learning and machine learning related areas.

Authors and Affiliations

Sajal Kaushik
Bharati Vidyapeeth's College of Engineering, A-4, Paschim Vihar, Rohtak Road, New Delhi, India
Pulkit Kogat
Bharati Vidyapeeth's College of Engineering, A-4, Paschim Vihar, Rohtak Road, New Delhi, India
Dr. Narina Thakur
Bharati Vidyapeeth's College of Engineering, A-4, Paschim Vihar, Rohtak Road, New Delhi, India

Bayesianprogramming(bp), pertitentvariables (P), decomposition(d), Bayesian networks(bn), searching(s).

  1. Bishop, C. M. Pattern Recognition and Machine Learning (Springer,2006).
  2. Murphy, K. P. Machine Learning: A Probabilistic Perspective (MIT Press, 2012).
  3. Ghahramani, Z. Bayesian nonparametrics and the probabilistic approach to modelling. Phil. Trans. R. Soc. A 371, 20110553 (2013).
  4. Jaynes, E. T. Probability Theory: the Logic of Science (Cambridge Univ. Press, 2003).
  5. Koller, D. & Friedman, N. Probabilistic Graphical Models: Principles and Techniques (MIT Press, 2009).
  6. Doya, K., Ishii, S., Pouget, A. & Rao, R. P. N. Bayesian Brain: Probabilistic Approaches to Neural Coding (MIT Press, 2007).
  7. Jordan, M., Ghahramani, Z., Jaakkola, T. & Saul, L. An introduction to variational methods in graphical models. Mach. Learn. 37, 183–233 (1999).
  8. Pfeffer, A. Practical Probabilistic Programming (Manning, 2015).
  9. Bishop, C. M. Model-based machine learning. Phil. Trans. R. Soc. A 371, 20120222 (2013).
  10. Mansinghka, V., Selsam, D. & Perov, Y. Venture: a higher-order probabilistic programming platform with programmable inference. Preprint at http://arxiv. org/abs/1404.0099 (2014).
  11. MacKay, David JC. "Developments in probabilistic modelling with neural networks-ensemble learning." In Neural Networks: Artificial Intelligence and Industrial Applications. Proc. of the 3rd Annual Symposium on Neural Networks,pp.191-198. 1995.
  12. Gal, Yarin, Rowan Thomas McAllister, and Carl Edward Rasmussen. "Improving PILCO with bayesian neural network dynamics models." In Data-Efficient Machine Learning workshop, vol. 951, p. 2016. 2016.
  13. Woolrich, Mark W., Saad Jbabdi, Brian Patenaude, Michael Chappell, Salima Makni, Timothy Behrens, Christian Beckmann, Mark Jenkinson, and Stephen M. Smith."Bayesian analysis of neuroimaging data in FSL." Neuroimage 45, no. 1 (2009): S173-S186.
  14. Bunt, Andrea, and Cristina Conati. "Probabilistic student modelling to improve exploratory behaviour." User Modeling and User-Adapted Interaction 13, no. 3 (2003): 269-309.
  15. Box, GEORGE EP. "Sampling and Bayes’ inference in scienti?c modelling and robustness." Journal of the Royal Statistical Society A 143 (1980): 383-430.
  16. Caimo, Alberto, Francesca Pallotti, and Alessandro Lomi. "Bayesian exponential random graph modelling of interhospital patient referral networks." Statistics in Medicine (2017).
  17. Gallagher, Marcus, Ian Wood, Jonathan Keith, and George Sofronov. "Bayesian inference in estimation of distribution algorithms." In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, pp. 127-133. IEEE, 2007.
  18. Garcia, Patricio, Analia Amandi, Silvia Schiaffino, and Marcelo Campo. "Evaluating Bayesian networks’ precision for detecting students’ learning styles." Computers & Education 49, no. 3 (2007): 794-808.
  19. Hullermeier, Eyke. "Toward a probabilistic formalization of case-based inference." In IJCAI, pp. 248-253. 1999.
  20. Kulatilake, Pinnaduwa HSW, Tien H. Wu, and Deepa N. Wathugala. "Probabilistic modelling of joint orientation." International Journal for Numerical and Analytical Methods in Geomechanics 14, no. 5 (1990): 325-350.
  21. Diard, Julien, Pierre Bessiere, and Emmanuel Mazer. "A survey of probabilistic models using the bayesian programming methodology as a unifying framework." (2003).
  22. Li, Jian, Shaogang Gong, and Tao Xiang. "Global Behaviour Inference using Probabilistic Latent Semantic Analysis." In BMVC, vol. 3231, p. 3232. 2008.
  23. Kosgodagan, Alex, et al. "A 2-dimension dynamic Bayesian network for large-scale degradation modelling with an application to a bridges network." (2017).
  24. Jaeger, Manfred. "On the complexity of inference about probabilistic relational models." Artificial Intelligence 117, no. 2 (2000): 297-308.
  25. Beaumont, Paul, and Michael Huth. "Constrained Bayesian Networks: Theory, Optimization, and Applications." arXiv preprint arXiv:1705.05326(2017).
  26. Weber, Philippe, and Lionel Jouffe. "Reliability modelling with dynamic bayesian networks." IFAC Proceedings Volumes 36, no. 5 (2003): 57-62.
  27. Hunter, Anthony. "A probabilistic approach to modelling uncertain logical arguments." International Journal of Approximate Reasoning 54, no. 1 (2013):47-81.
  28. Ng, Kee Siong, John W. Lloyd, and William TB Uther. "Probabilistic modelling, inference and learning using logical theories." Annals of Mathematics and Artificial Intelligence 54, no. 1-3 (2008): 159-205.
  29. Chater, Nick, Joshua B. Tenenbaum, and Alan Yuille. "Probabilistic models of cognition: Conceptual foundations." Trends in cognitive sciences 10, no. 7 (2006): 287-291.
  30. Oakley, Jeremy E., and Anthony O'Hagan. "Probabilistic sensitivity analysis of complex models: a Bayesian approach." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, no. 3 (2004): 751-769.
  31. Riezler, Stefan. "Statistical inference and probabilistic modelling for constraint-based nlp." arXiv preprint cs/9905010 (1999).
  32. Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
  33. Nilsson, Mikael, and Marcus Ejnarsson. "Speech recognition using hidden markov model." (2002).
  34. Nguyen, Nam Thanh, Dinh Q. Phung, Svetha Venkatesh, and Hung Bui. "Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model." In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2, pp. 955-960. IEEE, 2005.
  35. Sha, Fei, and Lawrence K. Saul. "Large margin hidden Markov models for automatic speech recognition." In Advances in neural information processing systems, pp. 1249-1256. 2007.
  36. Bunke, H. and Caelli, T. eds., 2001. Hidden Markov models: applications in computer vision (Vol. 45). World Scientific.

Publication Details

Published in : Volume 2 | Issue 7 | September 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 01-07
Manuscript Number : CSEIT174401
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sajal Kaushik, Pulkit Kogat, Dr. Narina Thakur, "Probabilistic Modeling in Machine Learning and Artificial Intelligence", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.01-07, September-2017.
Journal URL : http://ijsrcseit.com/CSEIT174401

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