Search Results for "bayesian python"
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Showing 3 open source projects for "bayesian python"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Power through agendas and documents, make more informed decisions and conduct board meetings faster.iBabs not only captures the entire decision-making process – it takes all the paperwork out of meetings. iBabs empowers everyone who has ever organized or attended, a meeting. With a seemingly simple app that offers complete control and a comprehensive overview of all those fiddly details. With about 3000 organizations and over 300,000 users, iBabs gives you peace of mind. So you can quickly organize effective meetings, and good decisions can be made with confidence. iBabs didn’t just happen overnight. We started analyzing and simplifying board meeting processes many years ago. We understand all the work that goes into meetings, and how to streamline everything so it all flows smoothly. On any device, confidentially, securely and automatically. Make good decisions with confidence.
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Bayesian Optimization
Python implementation of global optimization with gaussian processes
This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic... -
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ArviZ.jl
Exploratory analysis of Bayesian models with Julia
ArviZ.jl (pronounced "AR-vees") is a Julia package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, model checking, comparison and diagnostics. -
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Bayesian Julia
Bayesian Statistics using Julia and Turing
Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from...
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