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<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=648880075207035&amp;ev=pageview&amp;noscript=1"> bayesia s.a.s. the bayesia product portfolio bayesialab—your desktop analytics and research laboratory best—bayesian expert system for troubleshooting bricks—bayesian representation and inference for complex knowledge structuring the bayesia story faq privacy policy bayesialab software bayesialab feature overview what's new in bayesialab 7? bayesialab introduction knowledge modeling discrete, nonlinear, and nonparametric modeling unsupervised learning supervised learning clustering inference model utilization knowledge communication bayesialab vr module bayesialab websimulator bayesia engine api bayesia expert knowledge elicitation environment (bekee) bayesia market simulator licensing options & pricing pricing request a quote academic edition education package download demo apply for evaluation version schedule a bayesialab demo bayesialab documentation bayesialab user guide bayesialab installation bayesia license server bayesialab websimulator bayesia engine api bayesian networks in theory & practice bayesialab book (1st edition, 2015) table of contents & download link data for chapter 5: bayesian networks and data data for chapter 6: supervised learning data for chapter 7: unsupervised learning data for chapter 8: probabilistic structural equation models bayesialab book (draft of 2nd edition) bayesian network theory theoretical introduction examples: non-causal, causal, and temporal joint probability distribution evidential and causal reasoning seminar & webinar recordings webinar: diagnostic decision support with bayesian networks webinar: analyzing the general social survey with bayesialab's unsupervised learning algorithms webinar: predicting injury severity from crash data for triage optimization webinar: probabilistic reasoning under uncertainty with bayesian networks webinar: bayesian networks for risk management without data webinar: optimizing health policies with bayesian networks webinar: quantifying product cannibalization webinar: marketing mix modeling & optimization webinar: analyzing capital flows of exchange-traded funds webinar: causality & certainty in criminal sentencing webinar: geographic optimization with bayesialab webinar: health outcomes research with bayesialab webinar: probabilistic latent factor induction with bayesian networks seminar: bayesian networks for health economics and public policy research seminar: knowledge discovery in financial data with bayesian networks seminar: bayesian networks—artificial intelligence for research, analytics, and reasoning seminar: knowledge elicitation & reasoning under uncertainty seminar: key drivers analysis with bayesian networks and bayesialab seminar: causality for policy assessment and impact analysis seminar: evidential reasoning with bayesian networks video preview: introductory bayesialab course bayesialab conference videos 2017 bayesialab conference in paris 2016 bayesialab conference in nashville 2015 bayesialab conference in fairfax 2014 bayesialab conference in los angeles 2013 bayesialab conference in orlando examples & tutorials knowledge modeling quick start guide knowledge discovery quick start demo bayesian network as an expert system quick start demo websimulator quick start guide tutorial on loyalty driver analysis example: simpson's paradox academic literature publications by bayesia's principals research conducted with the bayesialab software platform research by field of study research by analysis type courses & events 2018 event calendar webinars & seminars august 17: webinar building a technical fault diagnosis system with bayesialab september 4–5: intelligence & national security summit in national harbor, md september 11: seminar in arlington, va bayesian networks for intelligence analysis september 21: webinar adversarial reasoning with bayesian networks professional courses august 29–31, 2018: london introductory bayesialab course september 3–5, 2018: london advanced course september 26–28, 2018: new delhi introductory bayesialab course october 29–31, 2018: chicago introductory bayesialab course november 5–7, 2018: chicago advanced bayesialab course december 5-7, 2018: new york city introductory bayesialab course 6th annual bayesialab conference in chicago october 29–31, 2018: introductory bayesialab course november 1–2, 2018: bayesialab conference november 5–7, 2018: advanced bayesialab course call for presentations bayesialab store europe & rest of world north america forum news bayesialab 7 artificial intelligence for research, analytics, and reasoning what is bayesialab? built on the foundation of the bayesian network formalism, bayesialab 7.0 is a powerful desktop application (windows, macos, linux/unix) with a highly sophisticated graphical user interface. it provides scientists a comprehensive “lab” environment for machine learning, knowledge modeling, diagnosis, analysis, simulation, and optimization. with bayesialab, it has become feasible for applied researchers in many fields, rather than just computer scientists, to take advantage of the bayesian network formalism. bayesialab builds upon the inherently graphical structure of bayesian networks and provides highly advanced visualization techniques to explore and explain complex problems. as a result, a broad range of stakeholders, regardless of their quantitative skill, can engage with a bayesian network model and contribute their expertise. what is a bayesian network? invented by judea pearl in the 1980s at ucla, bayesian networks are a mathematical formalism that can simultaneously represent a multitude of probabilistic relationships between variables in a system. the graph of a bayesian network contains nodes (representing variables) and directed arcs that link the nodes. the arcs represent the relationships of the nodes. whereas traditional statistical models are of the form y=f(x), bayesian networks do not have to distinguish between independent and dependent variables. rather, a bayesian network approximates the entire joint probability distribution of the system under study. the bayesialab workflow in practice researchers can use bayesialab to encode their domain knowledge into a bayesian network. alternatively, bayesialab can machine-learn a network structure purely from data collected from the problem domain. irrespective of the source, a bayesian network becomes a representation of the underlying, often high-dimensional problem domain. the researcher can then use bayesialab to carry out “omni-directional inference,” i.e., reason from cause to effect (simulation), or from effect to cause (diagnosis), within the bayesian network model. on this basis, bayesialab offers an extensive analytics, simulation and optimization toolset, providing comprehensive support for policy development and decision making. in this context, bayesialab is unique in its ability to distinguish between observational and causal inference. thus, decision makers can correctly simulate the consequences of actions not yet taken. key features of bayesialab expert knowledge modeling unsupervised structural learning supervised learning observational inference causal inference diagnosis, prediction, and simulation optimization virtual reality support some of our happy clients bayesialab has been able to accelerate our consumer modeling in family care by cutting costs and enabling model creation in minutes – not months. beyond that, it has allowed us to take our traditional approaches into new territories: virtual product design & testing, in!uencing copy development and even volume forecasting. it has been the single biggest enabler of deeper consumer insights & more actionable modeling across our business.” prabhath nanisetty procter & gamble (usa), family care cmk for north america bayesia usa 305 lockhart court fr

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http://www.bayesialab.com/machine-learning#supervised
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  CHANGED 2014-03-21

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