Search results for Bayesian Modeling

Bayesian Basics, Explained">Bayesian Basics, Explained
...Bayesian or otherwise, by looking at what it does to the data, and the best available method for any particular problem might well be set up in a nonBayesian way. KG: Does Bayesian inference have a special role in Big Data or the Internet of Things? AG: Yes, I think so. The essence of Bayesian...https://www.kdnuggets.com/2016/12/bayesianbasicsexplained.html

Bayesian Machine Learning, Explained">Bayesian Machine Learning, Explained
...onparametric Bayesian model, and provides efficient samplers for every conditional distribution. CrossCat combines strengths of nonparametric mixture modeling and Bayesian network structure learning: it can model any joint distribution given enough data by positing latent variables, but also...https://www.kdnuggets.com/2016/07/bayesianmachinelearningexplained.html

Frequentists Fight Back
...of the term is often unclear. Is it objective Bayes, subjective Bayes, approximate Bayes, empirical Bayes, or all of the above? Are Bayesian networks Bayesian? Bayesian methods are complicated and confusing even to academic statisticians! In the "true" Bayesian approach, priors should be set before...https://www.kdnuggets.com/2018/05/frequentistsfightback.html

XGBoost and Random Forest® with Bayesian Optimisation
...n.model_selection import cross_val_score We define a function to run Bayesian optimization given data, function to optimize and its hyperparameters: #Bayesian optimization def bayesian_optimization(dataset, function, parameters): X_train, y_train, X_test, y_test = dataset n_iterations = 5 gp_params...https://www.kdnuggets.com/2019/07/xgboostrandomforestbayesianoptimisation.html

Introduction to Bayesian Inference
...basic understanding of statistics and probability, as well as some experience with Python. The examples use the Python package pymc3. Introduction to Bayesian Thinking Bayesian inference is an extremely powerful set of tools for modeling any random variable, such as the value of a regression...https://www.kdnuggets.com/2016/12/datascienceintroductionbayesianinference.html

Bayesian and Causal Software
...sion, segmentation, time series prediction, anomaly detection and more. Free trial and walkthroughs available. Bayesware Discoverer 1.0, an automated modeling tool able to extract a Bayesian network from data by searching for the most probable model BNet, includes BNet.Builder for rapidly creating...https://www.kdnuggets.com/software/bayesian.html

Statistical Modeling: A Primer
...orizations as well, for instance between timeseries or longitudinal modeling, in which our data span two or more points in time, and crosssectional modeling, in which we are only have data for one slice in time. Marketing mix modeling uses timeseries data whereas most marketing research surveys...https://www.kdnuggets.com/2017/03/statisticalmodelingprimer.html

Software Suites/Platforms for Analytics, Data Mining, Data Science, and Machine Learning
...atioN: modeling and optimization with "on the job learning" for business and engineering by Reactive Search SrL. LPA Data Mining tools support fuzzy, Bayesian and expert discovery and modeling of rules. Lumidatum, Data Science platform that enables personalization and predictive analytics inside...https://www.kdnuggets.com/software/suites.html

How Bayesian Networks Are Superior in Understanding Effects of Variables
…ilkinson, L., Blank, G, Gruber, C. (1995) Desktop Data Analysis with Systat, SPSS, Inc., Chicago, IL. Bayes Nets Cooper, G F, Herskovits, E, (1992) A Bayesian method for the induction of probabilistic networks from data, Machine Learning, 9 pp 309347 Pearl, J (1988) Probabilistic Reasoning in…https://www.kdnuggets.com/2017/11/bayesiannetworksunderstandingeffectsvariables.html

When not to use deep learning">When not to use deep learning
…r future. Tools for interpretation of deep learning models for images and discrete sequences are getting better. Recent software such as Edward marry Bayesian modeling and deep net frameworks, allowing for quantification of uncertainty of neural network parameters and easy Bayesian inference via…https://www.kdnuggets.com/2017/07/whennotusedeeplearning.html

Vital Statistics You Never Learned… Because They’re Never Taught
...probabilities of making assertions about effects being positive when in fact they are zero. Finally, there is the likelihood school which is like the Bayesian school without prior distributions. Likelihood methods like Bayesian ones avoid the sample space so are much cleaner, but they mainly...https://www.kdnuggets.com/2017/08/vitalstatisticsneverlearnednevertaught.html

Towards Automatic Text Summarization: Extractive Methods
...the diverse data sources. Nonetheless, the significant limitation of this method is that the CST relations should be explicitly determined by human. Bayesian Topic Models While other approaches do not have very clear probabilistic interpretations, Bayesian topic models are probabilistic models...https://www.kdnuggets.com/2019/03/towardsautomatictextsummarization.html

Key Algorithms and Statistical Models for Aspiring Data Scientists">Key Algorithms and Statistical Models for Aspiring Data Scientists
...10) Splinebased models (MARS...) for flexible modeling of processes 11) Markov chains and stochastic processes (alternative approach to time series modeling and forecast modeling) 12) Missing data imputation schemes and their assumptions (missForest, MICE...) 13) Survival analysis (very helpful...https://www.kdnuggets.com/2018/04/keyalgorithmsstatisticalmodelsaspiringdatascientists.html

Madrid Summer School 2014 on Advanced Statistics and Data Mining
...ds. Introduction to Robust Statistics. Course 8: Bayesian Classifiers (15 h) Discrete predictors. Gaussian Bayesian networksbased classifiers. Other Bayesian classifiers. Bayesian classifiers for: positive and unlabeled data, semisupervised learning, data streams, temporal data. 2nd session:...https://www.kdnuggets.com/2014/04/madridsummerschool2014onadvancedstatisticsdatamining.html

Optimizing Web sites: Advances thanks to Machine Learning
…many more product profiles than this. But often this was not enough for complex products or services. Also, another modeling method, discrete choice modeling, supplanted conjoint. Discrete choice modeling allowed the evaluationof products and services in the context of competitive offerings. This…https://www.kdnuggets.com/2017/07/optimizingwebsitesmachinelearning.html

DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models
...ks in the market, DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset. Causal Bayesian Networks as a Visual Representation of Unfairness Causal Bayesian Networks(CBNs) are a statistical technique used to represent...https://www.kdnuggets.com/2019/10/deepmindusingoldtechniqueevaluatefairnessmachinelearningmodels.html

The Current State of Automated Machine Learning
...projects are open source, written in Python, and aimed at simplifying a machine learning process by way of AutoML, in contrast to Autosklearn using Bayesian optimization, TPOT's approach is based on genetic programming. While the approach is different, however, the outcome is the same: automated...https://www.kdnuggets.com/2017/01/currentstateautomatedmachinelearning.html

Online Courses in Predictive Analytics, Machine Learning, Data Science from Statistics.com
...s (used in CRM, online advertising and brand analysis). Taught by author Nitin Indurkhya, who first coined the term Big Data in 1998. Sep 26  Oct 24 Bayesian Statistics in R (4 weeks, online) Learn how to run Bayesian regression models in R  linear, linear regression, poisson, logit and negative...https://www.kdnuggets.com/2014/01/onlinecoursespredictiveanalyticsmachinelearningdatasciencestatisticscom.html

Statistical Data Analysis in Python
...Plotting and Visualization Plotting in Pandas vs Matplotlib Bar plots Histograms Box plots Grouped plots Scatterplots Trellis plots Statistical Data Modeling Statistical modeling Fitting data to probability distributions Fitting regression models Model selection Bootstrapping Required Packages...https://www.kdnuggets.com/2016/07/statisticaldataanalysispython.html

Top July stories: Bayesian Machine Learning, Explained; Why Big Data is in Trouble: They Forgot About Applied Statistics
...verlook SAS vs R vs Python: Which Tool Do Analytics Pros Prefer? Data Mining History: The Invention of Support Vector Machines Text Mining 101: Topic Modeling 5 Deep Learning Projects You Can No Longer Overlook Most shared Why Big Data is in Trouble: They Forgot About Applied...https://www.kdnuggets.com/2016/08/topstories2016jul.html

KDnuggets™ News 13:n17, July 17
...cation. Apply by July 31. PhD Student, Mixing MetaModeling and DataMining at U. Haute Alsace, France  Jul 12, 2013. Ph. D. Research on mixing metamodeling and datamining for explicit modeling of user traces, digital footprints and online reputation. Funding is available for 36 months. 2...https://www.kdnuggets.com/2013/n17.html

The MBA Data Science Toolkit: 8 resources to go from the spreadsheet to the command line
...nondebate), I think that a solid foundation in Bayesian reasoning and statistics is a crucial part of any data scientist’s repertoire. For example, Bayesian reasoning underpins much of modern A/B testing and Bayesian methods are applied in many other areas of data science (and are generally...https://www.kdnuggets.com/2016/04/mbadatasciencetoolkit.html

More Free Data Mining, Data Science Books and Resources
...tion stuff based on the Bayesian networks perspective. Many machine learning concepts are approached and exemplified. Probabilistic Programming & Bayesian Methods for Hackers by Cam DavidsonPilon, 2013.A book about Bayesian networks that provide capabilities to solve very complex problems....https://www.kdnuggets.com/2015/03/freedataminingdatasciencebooksresources.html

If you’re a developer transitioning into data science, here are your best resources"> If you’re a developer transitioning into data science, here are your best resources
...h next: working with structured data working with unstructured data classifying relationships in knowledge graphs (see a good resource here) learning bayesian probability and modeling approaches work on an NLP project (so text data) We’re not recommending that you apply to the companies you find...https://www.kdnuggets.com/2019/06/developertransitioningdatasciencebestresources.html

PhD position in machine learning
…dge of some numerical programming language (R/Matlab/Python). Prior experience with machine learning and Bayesian models, in particular nonparametric Bayesian modeling, is considered a strong asset. The monthly salary will be based on rules of the salary system applied in Finnish universities, and…https://www.kdnuggets.com/academic/13/0705hiitdoctoralstudentinmachinelearning.html

Best Data Science Online Courses
…ian Methods for Hackers, you can expect some very interesting videos on useful statistics for Data Science. Screencasts: A/B Testing Conversion Rates Bayesian BetaBinomial Model Bayesian Modelling (Car Arrival Problem) Create Markov Chains Using Your Chrome Browsing History Estimating the Hazard…https://www.kdnuggets.com/2015/10/bestdatascienceonlinecourses.html

Why You Should Attend the Data Science Summit 2016 and 9 Talks To Be Excited About
...a S.B. in 2004 and Ph.D. in 2009 from the Department of Electrical Engineering and Computer Science at MIT. Her research interests are in largescale Bayesian dynamic modeling and computations. She has received numerous awards: Sloan Research Fellowship (2015) ONR Young Investigator award (2015)...https://www.kdnuggets.com/2016/07/datasciencesummit20169talks.html

Legendary Entertainment, Applied Analytics: Quantitative Researcher
...large, complex datasets Expertise with one or more of the following machine learning and statistical modeling domains: quantitative textual analysis, Bayesian modeling, statistical or network analysis of social media data, image analysis and recognition Available to begin late 2015 ...https://www.kdnuggets.com/jobs/15/1023legendaryquantitativeresearcher.html

AI Knowledge Map: How To Classify AI Technologies">AI Knowledge Map: How To Classify AI Technologies
...specific variable but rather works with probabilistic models. Bayesian Program Synthesis (BPS) is somehow a form of probabilistic programming, where Bayesian programs write new Bayesian programs (instead of humans do it, as in the broader probabilistic programming approach) Ambient Intelligence...https://www.kdnuggets.com/2018/08/aiknowledgemapclassifyaitechnologies.html

SpatioTemporal Statistics: A Primer
...think it will be a challenge to integrate the innovations that are coming from the engineering and computer science disciplines with the statistical modeling. I also think there is a great need to increase the complexity of our models in order to capture how multiple spatiotemporal processes are...https://www.kdnuggets.com/2019/04/spatiotemporalstatisticsprimer.html

Clustering and Segmentation Software
...ionic ESOM Tools, a suite of programs for clustering, visualization, and classification with Emergent SelfOrganizing Maps (ESOM). David Dowe Mixture Modeling page for modeling statistical distribution by a mixture (or weighted sum) of other distributions. ELKI: Environment for Developing...https://www.kdnuggets.com/software/clustering.html

Aalto: Postdocs in machine learning and related areas
...computational molecular biology and medicine Particularly suitable topics are: Ab initio protein structure prediction from many amino acid sequences; Bayesian modelling of evolution; Bayesian modelling in metabolomics; Bayesian population genetics and genomics; Biological applications of compressed...https://www.kdnuggets.com/academic/13/0213aaltopostdocsmachinelearningrelatedareas.html

Additions to KDnuggets Directory in October
...IBM Multimedia Analysis and Retrieval System (IMARS), can index, classify, and search large collections of digital images and videos. In Software :: Bayesian Networks and Bayesian Classifiers Chordalysis, a loglinear analysis method for big data, which exploits recent discoveries in graph theory...https://www.kdnuggets.com/2013/11/addedtokdnuggetsinoctober.html

60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more
…ce, and Learning Algorithms [Buy on Amazon] David J.C. MacKay, 2005 Data Mining and Business Analytics with R [Buy on Amazon] Johannes Ledolter, 2013 Bayesian Reasoning and Machine Learning [Buy on Amazon] David Barber, 2014 Gaussian Processes for Machine Learning [Buy on Amazon] C. E. Rasmussen…https://www.kdnuggets.com/2015/09/freedatasciencebooks.html

KDnuggets Top Blogger: An Interview with Brandon Rohrer, Top Data Scientist
...the great content you consistently share with our readers, as well as on your own blog and beyond. Brandon Rohrer recent KDnuggets posts include: How Bayesian Inference Works  15 Nov 2016 Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief,...https://www.kdnuggets.com/2017/02/topbloggerinterviewbrandonrohrerdatascience.html

Neural Network Software for Classification
...complex relationships, recognizing unknown patterns and predicting actual trends from data sets. NeuroSolutions, powerful and flexible neural network modeling software. NeuroXL, neural networks software for classification and prediction of simple and complex data in Excel. NeuralWorks Predict...https://www.kdnuggets.com/software/classificationneural.html

Open Source Data Science Masters Curriculum
..., NumPy, SciPy, matplotlib and IPython Data Science in iPython Notebooks (Linear Regression, Logistic Regression, Random Forests, KMeans Clustering) Bayesian Inference  pymc Labeled data structures objects, statistical functions, etc pandas (See: Python for Data Analysis) Python wrapper for the...https://www.kdnuggets.com/2013/12/opensourcedatasciencemasterscurriculum.html

Globys: Applied Data Scientist, Machine Learning and Data Science
...n this role you will need: Knowledge in a variety Machine Learning and Data Mining techniques, such as: Pattern classification, statistical learning, Bayesian Decision Theory, parameter estimation, support vector machines, neural networks, regularization techniques, decision trees, regression...https://www.kdnuggets.com/jobs/14/0123globysapplieddatascientistmachinelearningdatascience.html