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# Search results for Markov Chain Monte Carlo

Found 11 documents, 11104 searched:

• ### Practical Markov Chain Monte Carlo

comments By Gregory Janesch, Statistics Graduate Student I’ve seen a number of examples of MCMC algorithms, and while they’re all solid, a lot of them tend to be a bit too neat - they have a fairly simple model, a single predictor (maybe two), and not much else. This one is a good example, as it...

https://www.kdnuggets.com/2020/06/practical-markov-chain-monte-carlo.html

• ### Essential Resources to Learn Bayesian Statistics">Essential Resources to Learn Bayesian Statistics

...ns of Bayesian statistics. One final thing that is a hard requirement and a common thread between the references and resources listed above is Markov Chain Monte Carlo (MCMC). To fully explore probability spaces and distributions, you need efficient and reliable computational methods like MCMC and...

https://www.kdnuggets.com/2020/07/essential-resources-learn-bayesian-statistics.html

• ### Most Viewed Machine Learning Talks at Videolectures

...Probability and Graphical Models, Sam Roweis, 41039 views, 1:02:45 (February 25, 2007, at Machine Learning Summer School (MLSS), Taipei 2006) Markov Chain Monte Carlo, Iain Murray, 37223 views, 2:27:56 (November 2, 2009, at Machine Learning Summer School (MLSS), Cambridge 2009) Gaussian Process...

https://www.kdnuggets.com/2014/09/most-viewed-machine-learning-talks-videolectures.html

• ### Interview: Pedro Domingos: the Master Algorithm, new type of Deep Learning, great advice for young researchers

...always tractable; it takes a single pass through the network, and avoids all the difficulties and unpredictability of approximate methods like Markov chain Monte Carlo and loopy belief propagation. As a result, the learning itself, which in these deep models uses inference as a subroutine, also...

https://www.kdnuggets.com/2014/08/interview-pedro-domingos-master-algorithm-new-deep-learning.html

• ### Deep Learning in Neural Networks: An Overview

...l RL framework to play several Atari 2600 computer games directly from 84×84 pixel 60Hz video input… Even better results are achieved by using (slow) Monte Carlo tree planning to train comparatively fast deep NNs. For many situations the MDP assumption is unrealistic. “However, memories of previous...

https://www.kdnuggets.com/2016/04/deep-learning-neural-networks-overview.html

• ### Top 10 Big Ideas in Harvard Statistics 110 Class

...a remarkably beautiful and useful stochastic process. They were first studied by Markov as part of a philosophical debate about religion and free will, as a way to go beyond i.i.d. But in recent years they have proven worthwhile in a vast assortment of problems, especially through Markov chain...

https://www.kdnuggets.com/2013/12/top-10-big-ideas-harvard-statistics-110-class.html

• ### Globys: Research Scientist – Dynamic Bayesian Networks

...support vector machines, neural networks, decision trees Mathematical Programming, including linear, quadratic and semi-definite programming, Markov Chain Monte Carlo and Variational Methods Proficient in Python including NumPy, SciPy, scikit-learn and other packages that enable data science and...

https://www.kdnuggets.com/jobs/14/03-31-globys-research-scientist-dynamic-bayesian-networks.html

• ### Top 20 Python Machine Learning Open Source Projects, updated">Top 20 Python Machine Learning Open Source Projects, updated

…6, Contributors: 40, Github URL: Orange3 Pymc is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Commits: 2701, Contributors: 37, Github URL:…

https://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html

• ### Bayes Theorem for Computer Scientists, Explained

…ideas belong to a broader school of thought called Bayesian statistics which helps us build advanced statistical models using techniques like Markov Chain Monte Carlo methods and the No-U-Turn sampler. If you would like to try these techniques out, I recommend you use an open source library like…

https://www.kdnuggets.com/2016/02/bayes-theorem-computer-scientists-explained.html

• ### Top 30 Social Network Analysis and Visualization Tools

…odel estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. SVAT (Smart Visual Analytics Tool) is for data visualization, fraud investigation, and more. It provides…

https://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.html

• ### A simple approach to anomaly detection in periodic big data streams

...is based on an educated guess. Nevertheless, an optimal discretization provided the data can be determined e.g. using simulated annealing or a Markov Chain Monte Carlo procedure. In the case presented here, we segmented the week into weekdays and weekends. Each segment is then broken down into an...

https://www.kdnuggets.com/2016/08/anomaly-detection-periodic-big-data-streams.html