Elaboration of posteriori probability estimates for local inference in algebraic Bayesian networks in case of imprecise evidence

Banyak cara untuk dapat mendeteksi keamanan sebuah wilayah tertentu. Salah satu cara pengamanan yang bisa digunakan adalah dengan menggunakan pemantauan berbasis video pengawasan (video surveillance). Sebenarnya, video pengawasan sudah banyak digunakan di Indonesia. Tetapi, umumnya video pengawasan ini hanya mampu merekam gambar, tanpa ada kemampuan pintar yakni, object tracking, object recognition dan object analyzing. Sehingga, hasil yang diharapkan kurang maksimal dan belum bisa membantu tugas pengawasan secara keseluruhan. Paper ini bertujuan untuk membuat algoritma dari object tracking yang ada pada video pengawasan sebagai rujukan pengembangan video pengawasan dengan kemampuan object recognition dan object analyzing. Masalah utama yang sering muncul dalam pembuatan object tracking adalah ketika terjadi occlusion (tumpang tindih) antara dua object dalam sebuah frame. Pada saat occlusion, object yang sama pada frame yang berbeda kemungkinan dapat dikenali sebagai object yang berbeda. Sehingga, proses object tracking akan menjadi terganggu. Bayesian Networks memungkinkan untuk membandingkan data yang didapat dari masing-masing object yang ada (likelihood) dengan data awal yang telah dimiliki (prior), dengan menghitung Maximum A-Posteriori Probability(MAP) yang dimiliki, sehingga object yang sama pada frame yang berbeda tetap akan dikenali sebagai object yang sama<h6 style="text-align: center;"> Abstract</h6>There are many ways/technique to detect the security/safety of fixed area. One of security technique that can be used is by using monitoring based on Video surveillance. In fact, this monitoring video has already been used in Indonesia. But, video surveillance, commonly, just can record images without any smart abilities, such as object tracking, object recognition and object analyzing. So, the expected result is not optimal and still not be able to help monitoring role totally. This research is aimed to make the algorithm of object trackingin video surveillance, in order to be reference for development of video surveillance with ability of object recognition and object analyzing. The main problem that frequently comes up on the making of object tracking is occlusion between two objects in a single frame. When occlusion is happened, same object in different frame probably can be recognized as two different objects. So, the process of object tracking can be disturbed. Bayesian Network is enable to compare data that got from every object (likelihood) with prior data that has already been provided by counting its Maximum A-Posteriori Probability (MAP), so same object in different frame are still be able to be recognized as same object.

Download Full-text

Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/797 ◽

2019 ◽

Author(s):

Tahrima Rahman ◽

Shasha Jin ◽

Vibhav Gogate

Keyword(s):

Bayesian Networks ◽

Graphical Models ◽

Prediction Accuracy ◽

Probabilistic Models ◽

Probability Distributions ◽

Main Idea ◽

Exact Inference ◽

Trade Off ◽

Probability Estimates ◽

Prediction Time

Recently there has been growing interest in learning probabilistic models that admit poly-time inference called tractable probabilistic models from data. Although they generalize poorly as compared to intractable models, they often yield more accurate estimates at prediction time. In this paper, we seek to further explore this trade-off between generalization performance and inference accuracy by proposing a novel, partially tractable representation called cutset Bayesian networks (CBNs). The main idea in CBNs is to partition the variables into two subsets X and Y, learn a (intractable) Bayesian network that represents P(X) and a tractable conditional model that represents P(Y|X). The hope is that the intractable model will help improve generalization while the tractable model, by leveraging Rao-Blackwellised sampling which combines exact inference and sampling, will help improve the prediction accuracy. To compactly model P(Y|X), we introduce a novel tractable representation called conditional cutset networks (CCNs) in which all conditional probability distributions are represented using calibrated classifiers—classifiers which typically yield higher quality probability estimates than conventional classifiers. We show via a rigorous experimental evaluation that CBNs and CCNs yield more accurate posterior estimates than their tractable as well as intractable counterparts.

Download Full-text

Using a Bayesian Network to Predict L5/S1 Spinal Compression Force from Posture, Hand Load, Anthropometry, and Disc Injury Status

Applied Bionics and Biomechanics ◽

10.1155/2017/2014961 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Richard E. Hughes

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Bayesian Networks ◽

Biomechanical Model ◽

Biomechanical Modeling ◽

Compression Force ◽

Probability Estimates ◽

Spinal Compression ◽

Disc Injury ◽

Injury Status

Stochastic biomechanical modeling has become a useful tool most commonly implemented using Monte Carlo simulation, advanced mean value theorem, or Markov chain modeling. Bayesian networks are a novel method for probabilistic modeling in artificial intelligence, risk modeling, and machine learning. The purpose of this study was to evaluate the suitability of Bayesian networks for biomechanical modeling using a static biomechanical model of spinal forces during lifting. A 20-node Bayesian network model was used to implement a well-established static two-dimensional biomechanical model for predicting L5/S1 compression and shear forces. The model was also implemented as a Monte Carlo simulation in MATLAB. Mean L5/S1 spinal compression force estimates differed by 0.8%, and shear force estimates were the same. The model was extended to incorporate evidence about disc injury, which can modify the prior probability estimates to provide posterior probability estimates of spinal compression force. An example showed that changing disc injury status from false to true increased the estimate of mean L5/S1 compression force by 14.7%. This work shows that Bayesian networks can be used to implement a whole-body biomechanical model used in occupational biomechanics and incorporate disc injury.

Download Full-text