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).

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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.
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