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Discriminating between causal structures in Bayesian Networks given partial observations

Philipp Moritz, Jörg Reichardt, Nihat Ay
2014 Kybernetika (Praha)  
INFERENCE OF COMMON ANCESTORS In this section we briefly summarize the existing common cause principles and explain how they can be used to discriminate between partially observed Bayesian networks.  ...  DISCRIMINATING BAYESIAN NETWORKS Now we describe how this theorem can be used to discriminate between two causal hypotheses. Take the Bayesian networks from Figure 8 (a) and (b) as an example.  ... 
doi:10.14736/kyb-2014-2-0284 fatcat:jcvbb6rcz5dxzfs7jsqvo2soua

Exposing the probabilistic causal structure of discrimination

Francesco Bonchi, Sara Hajian, Bud Mishra, Daniele Ramazzotti
2017 International Journal of Data Science and Analytics  
The result is a type of constrained Bayesian network, which we dub Suppes-Bayes Causal Network (SBCN).  ...  In this paper we take a principled causal approach to the data mining problem of discrimination detection in databases.  ...  We start by observing that, while reconstructing a Bayesian Network is a known NP-hard problem, when an ordering among the variables in the network is given, finding the maximum likelihood network is not  ... 
doi:10.1007/s41060-016-0040-z dblp:journals/ijdsa/BonchiHMR17 fatcat:gbfeiopxjnhevf5nj2jgwkpfxe

Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos

Guillaume Gravier, Claire-Hélène Demarty, Siwar Baghdadi, Patrick Gros
2012 Multimedia tools and applications  
We investigate the use of structure learning in Bayesian networks for a complex multimodal task of action detection in soccer videos.  ...  We illustrate that classical score-oriented structure learning algorithms, such as the K2 one whose usefulness has been demonstrated on simple tasks, fail in providing a good network structure for classification  ...  Acknowledgments This work was partially funded by OSEO, French state agency for innovation, in the framework of the Quaero project.  ... 
doi:10.1007/s11042-012-1169-y fatcat:dgjjd5agwnhzdnld4pfe6nkova

Bayesian networks, Bayesian learning and cognitive development

Alison Gopnik, Joshua B. Tenenbaum
2007 Developmental Science  
McDonnell Foundation Causal Learning Research Collaborative, and the Paul E. Newton Career Development Chair (JBT).  ...  They discriminate between observations and interventions appropriately Kushnir & Gopnik, 2005) and use probabilities to calculate causal strength (Kushnir & Gopnik, 2005 .  ...  Learning causal Bayesian networks Three of the five papers in this section focus on children's causal learning.  ... 
doi:10.1111/j.1467-7687.2007.00584.x pmid:17444969 fatcat:fang2mkkyndermokwyt7tekvzi

Big data problems on discovering and analyzing causal relationships in epidemiological data

Yiheng Liang, Armin R. Mikler
2014 2014 IEEE International Conference on Big Data (Big Data)  
Recent advances in Computer Science, particularly in Bayesian networks, has initiated a renewed interest for causality research.  ...  Causal relationships can be possibly discovered through learning the network structures from data.  ...  A Bayesian network can be used to infer causal structures among variables. Note the difference between a Bayesian network and a causal network.  ... 
doi:10.1109/bigdata.2014.7004421 dblp:conf/bigdataconf/LiangM14 fatcat:zr4b3oxlurhf3b7tgiw3yf63ji

Maximum Margin Bayesian Networks [article]

Yuhong Guo, Dana Wilkinson, Dale Schuurmans
2012 arXiv   pre-print
In practice, the training technique allows one to combine prior knowledge expressed as a directed (causal) model with state of the art discriminative learning methods.  ...  The main difficulty is that the parameters in a Bayesian network must satisfy additional normalization constraints that an undirected graphical model need not respect.  ...  In each case, we formulated a Bayesian network topology that was intended to capture the causal structure of the domain, but in this case had no guarantee that the presumed structure was correct.  ... 
arXiv:1207.1382v1 fatcat:2wx7kwhp4vforgxxplazw7hoeu

Computational dynamic approaches for temporal omics data with applications to systems medicine

Yulan Liang, Arpad Kelemen
2017 BioData Mining  
In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working  ...  Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology.  ...  Bayesian approaches can be used in the case when there are more covariates than observations.  ... 
doi:10.1186/s13040-017-0140-x pmid:28638442 pmcid:PMC5473988 fatcat:rscvtjlpgrf53fbwlt6t4i22em

CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training [article]

Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath
2017 arXiv   pre-print
We consider the application of generating faces based on given binary labels where the dependency structure between the labels is preserved with a causal graph.  ...  First we train a causal implicit generative model over binary labels using a neural network consistent with a causal graph as the generator.  ...  In [26] , authors observe the connection between conditional GAN layers, and structural equation models.  ... 
arXiv:1709.02023v2 fatcat:w3xqayo2q5bubbvvtclnu2342a

Causal inference of gene regulation with subnetwork assembly from genetical genomics data

Chien-Hua Peng, Yi-Zhi Jiang, An-Shun Tai, Chun-Bin Liu, Shih-Chi Peng, Chun-Ta Liao, Tzu-Chen Yen, Wen-Ping Hsieh
2013 Nucleic Acids Research  
We demonstrate how effectively the inferred causality restores the regulatory structure of the networks that mediate lymph node metastasis in oral cancer.  ...  We introduce a method to reconstruct causal networks based on exploring phenotype-specific modules in the human interactome and including the expression quantitative trait loci (eQTLs) that underlie the  ...  Given n modules from the previous step, the Bayesian network can construct nðnÀ1Þ 2 local gene subnetworks.  ... 
doi:10.1093/nar/gkt1277 pmid:24322297 pmcid:PMC3950678 fatcat:ypuc4ekckjf5jczze7db2o5zzm

Predicting online participation through Bayesian network analysis

Elizaveta Kopacheva, Sergi Lozano
2021 PLoS ONE  
After fitting the parameters of the Bayesian network dependent on the received structure, it became evident that given prior knowledge of the explanatory factors that proved to be most important in terms  ...  This article scrutinises the causal relations between the variables associated with participation in online activism and introduces a three-step approach in learning a reliable structure of the participation  ...  When discussing the constraints of Bayesian network analysis to infer causal relations between the variables, the idea of causality must be also touched upon.  ... 
doi:10.1371/journal.pone.0261663 pmid:34941953 pmcid:PMC8699968 fatcat:ljias6rjyjh27hozdjenjdmkde

Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration [article]

Fattaneh Jabbari, Mahdi Pakdaman Naeini, Gregory F. Cooper
2017 arXiv   pre-print
Discovery of an accurate causal Bayesian network structure from observational data can be useful in many areas of science.  ...  In this paper, we introduce a novel framework to derive calibrated probabilities of causal relationships from observational data.  ...  In this paper, we focus on the discovery of causal Bayesian network (CBN) structure from observational data.  ... 
arXiv:1712.08626v1 fatcat:bxankr46v5emzjxwvo53valyxu

Beyond Tracking: Modelling Activity and Understanding Behaviour

Tao Xiang, Shaogang Gong
2006 International Journal of Computer Vision  
networks including a Multi-Observation Hidden Markov Model (MOHMM), a Parallel Hidden Markov Model (PaHMM) and a Coupled Hidden Markov Model (CHMM).  ...  Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scene-level behaviour interpretation.  ...  Given an observation sequence O and a model structure λ, we need to determine the model parameters θ (λ) = {A, B, π} that maximise the probability of the observation sequence given the model structure  ... 
doi:10.1007/s11263-006-4329-6 fatcat:jfg4mig2ureoxb5kbcocfvr5xm

On the Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles

Seif Eldawlatly, Yang Zhou, Rong Jin, Karim G. Oweiss
2010 Neural Computation  
In this work, we investigate the applicability of Dynamic Bayesian Networks (DBNs) in inferring the effective connectivity between spiking cortical neurons from their observed spike trains.  ...  The performance was assessed and compared to other methods under systematic variations of the network structure to mimic a wide range of responses typically observed in the cortex.  ...  In particular, Dynamic Bayesian Network (DBN) is one potential graphical method for identifying causal relationships between simultaneously observed random variables.  ... 
doi:10.1162/neco.2009.11-08-900 pmid:19852619 pmcid:PMC2794930 fatcat:tszm5pn6hrbxdo4uyp6ceyo2jy

Causal discovery in machine learning: Theories and applications

Ana Rita Nogueira, João Gama, Carlos Abreu Ferreira
2021 Journal of Dynamics & Games  
This temporal notion of past and future is often one of the critical points in discovering the causes of a given event.  ...  The purpose of this survey is to present a cross-sectional view of causal discovery domain, with an emphasis in the machine learning/data mining area. 2020 Mathematics Subject Classification.  ...  This research was carried out in the context of the project FailStopper (DSAIPA/DS/0086/2018) and supported by the Fundação para a Ciência e Tecnologia (FCT), Portugal for the PhD Grant SFRH/BD/146197/  ... 
doi:10.3934/jdg.2021008 fatcat:vh4dng5lsfcj3fydwbgy457ejm

Chapter 11 Bayesian Networks [chapter]

A. Darwiche
2008 Foundations of Artificial Intelligence  
There are two interpretations of a Bayesian network structure, a standard interpretation in terms of probabilistic independence and a stronger interpretation in terms of causality.  ...  The conditional probabilities of a Bayesian network quantify the dependencies between variables and their parents in the DAG.  ...  This dimension calls for distinguishing between learning generative versus discriminative Bayesian networks. To make this distinction more concrete, consider again the data set shown in Table 11 .5.  ... 
doi:10.1016/s1574-6526(07)03011-8 fatcat:yypbezypcbeazo6ehprmqrrdci
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