Category: Bayesian

Predicting Pharmacokinetics with Deterministic Models and Bayesian Statistics

From: https://towardsdatascience.com/predicting-pharmacokinetics-with-deterministic-models-and-bayesian-statistics-e3822a28977b Introduction Pharmacokinetics Pharmacokinetics (PK) deals with the distribution and metabolism of xenobioticsin organisms. In drug development and clinical research, one encounters pharmacologists whose job it is to advise physicians the dose of drug(s) to provide to their patients. For this purpose, they often run simulations of so-called pharmacokinetic models which are deterministic, i.e. they consist of ordinary differential equationswith … Continue reading Predicting Pharmacokinetics with Deterministic Models and Bayesian Statistics →

Bayesian Inference Algorithms: MCMC and VI

From: https://towardsdatascience.com/bayesian-inference-algorithms-mcmc-and-vi-a8dad51ad5f5 Unlike other areas of machine learning (ML), Bayesian ML requires us to know when an output is not trustworthy. When you train a regression or xgboost model, the model can be taken at face value given the settings and data. With Bayesian ML, the output is not guaranteed to be correct. Bayesian workflow … Continue reading Bayesian Inference Algorithms: MCMC and VI →

Bayesian Nonparametric Models

From: https://www.datasciencecentral.com/profiles/blogs/6448529:BlogPost:635167 Bayesian Nonparametrics is a class of models with a potentially infinite number of parameters. High flexibility and expressive power of this approach enables better data modelling compared to parametric methods. Bayesian Nonparametrics is used in problems where a dimension of interest grows with data, for example, in problems where the number of features … Continue reading Bayesian Nonparametric Models →

Simulating data with Bayesian networks

From: http://gradientdescending.com/simulating-data-with-bayesian-networks/ Bayesian networks are really useful for many applications and one of those is to simulate new data. Bayes nets represent data as a probabilistic graph and from this structure it is then easy to simulate new data. This post will demonstrate how to do this with bnlearn. Fit a Bayesian network Before simulating new … Continue reading Simulating data with Bayesian networks →

Bayesian Convolutional Neural Networks with Bayes by Backprop

From: https://medium.com/neuralspace/bayesian-convolutional-neural-networks-with-bayes-by-backprop-c84dcaaf086e So far, we have elaborated how Bayes by Backprop works on a simple feedforward neural network. In this post, I will explain how you can apply exactly this framework to any convolutional neural network (CNN) architecture you like. You might have seen Gal’s & Ghahramani’s (2015) publication of a Bayesian CNN, but that’s an entirely different approach … Continue reading Bayesian Convolutional Neural Networks with Bayes by Backprop →

Bayesian Statistics: Analysis of Health Data

From: https://datascienceplus.com/bayesian-statistics-analysis-of-health-data/ The premise of Bayesian statistics is that distributions are based on a personal belief about the shape of such a distribution, rather than the classical assumption which does not take such subjectivity into account. In this regard, Bayesian statistics defines distributions in the following way: Prior: Beliefs about a distribution prior to observing any … Continue reading Bayesian Statistics: Analysis of Health Data →