# Questions tagged [bayesian]

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### why naive is needed in Naive Bayes ,what happens if naive is not included in Bayes theorem?

Im trying to understand why naive is needed in Naive Bayes and everyone says Naive Bayes assumes the input features (predictors) are not correlated hence they are not dependent on each other . i want ...
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### Bayesian Network - a practical example of marginal probability calculation?

I was watching an online course on the topic Bayesian Networks and I have a question regarding the calculation of marginal probabilities. Hier is the given network: and the corresponding course video ...
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### If I use Gibbs sampling with a Bayesian model, what do I have to check is memoryless?

Right now I am trying to better understand how Bayesian modeling works with just the basics. I found through reading tutorials that some very basic Bayesian models like Bayesian Hierarchical Modeling ...
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### Is k-means with Mahalanobis a valid option for clustering?

I want more info into if k-means with Mahalanobis distance is a mathematically/methodologically correct option for datasets with different variance clusters. The steps are: Create aggregate datasets (...
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### How to make model for Multivariate timeseries using tensorflow probability structural model?

I have done modelling for Univariate timeseries but while using multivariate time series ( independent features) not able to ...
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### Bayesian/Frequentist “philosophies” in relation to common ML models

Starting to think about questions tout could come up in a data science job interview, there is this one question that is bugging me since i don't have a deep background in statistics. I have a ...
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### what R and Python package can predict a word list with certain probability after one known word?

I have a lot of documents, I need to know after one know word, which words occurs with which probability. For example: ...
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### Derivation of Bayes classifier in Murphy's book

I am reading Kevin Murphy's Machine Learning book (MLAPP, 1st printing) and want to know how he got the expression for the Bayes classifier using minimization of the posterior expected loss. He wrote ...
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### What are the tradeoffs between Bayesian Deep Learning and Deep Gaussain Processes?

I understand the differences between Deep Gaussian Processes(DGPs) and Bayesian Deep Learning(BDL): DGPs are essentially feed-forward neural networks where each node is a Gaussian Processes, which BDL ...
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### How to update the posterior belief when we are observing a stream of correlated data from a fixed but unknown data source

I want to build a [probabilistic] model that aims to infer the true value of an unknown categorical variable, $y \in \{1,2,..., K\}$. We have a dataset $(X,y): \mathbb{R}^d\rightarrow \{1,2,..., K\}$ ...
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### Good introductory reference for Bayesian Non-parametric (Dirichlet Process / Chinese Restaurant Process)

I am looking for a recommendation for basic introductory material on Bayesian Non-parametric methods, specifically Dirichlet Process / Chinese Restaurant Process. I am looking for material which ...
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### Tensorflow Probability Example : Gradient Computation Error

I am trying to run example code from tensorflow Probability for Dirichlet Process Mixture Model (https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/...
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### Bayes factor values

I have two samples A and B with continuous values such as results students. I want to calculate the baysian t test to get the Bayes values. I get the Bayes value of 0.16,so does it mean the null ...
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### Bayesian test for classification problems?

I am using two classification algorithms in Weka i.e. Logistic Regression and Naive Bayes and want to know which algorithm has better performance? I need a statistical test like Bayesian, so which ...
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### how to use Bayesian theorem and probabilistic analysis? [closed]

could someone help me to solve this problem please ? Your prize Rapid Ripe tomato plant has flowered and is ready to start producing fruit. If all goes well, ...
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### Is it possible not to make all mcmc process every time when we get new observation without big quality decrease?

I have a service that has to give a probabilistical distribution for some value in the nearest future. It works on MCMC-sampling algorithm (Stan sampling). Each time when this service is asked for an ...
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### Help in interpreting the results of the A / B test Bayessian approach for non binomial data

I would like to carry out an A / B test based on data regarding the profit from a given user. I found an interesting library for such tests https://github.com/tcassou/babtest. Now I would like to ask ...
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### Maximum A posteriori estimation of sequential single parameter

Assume you have a temperature sensor that outputs one single reading every minute. After 1000 reading you put all these values together and build a prior pdf which happens to be Gaussian. Now, you get ...
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### how to consider sampling when trying to inference exponential parameter

consider this case: There is a price rate for a certain product that changes throw time, The price rate is changed every x minutes. This price get sampled/observed in a non-uniform distribution ...
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### what is the representation/meaning/implication in real life of $P(\text{+})$ in the wiki Drug testing Example about Bayes' theorem

In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the probability of an event, based on prior knowledge of conditions that might be related to ...
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### Derive Bayes Theorem using Deep Learning

This may be a silly question, but has anyone tried to use deep learning to derive the actual theorem of Bayes? So, given some data, is it possible to show that a network has learned Bayes Theorem as ...
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### Bayes by Backprop

Since the bayes by backprop algorithm is modeling weight uncertainty by drawing samples from from a gaussian distribution, I wondered what the biggest difference to the following approach would be: I ...
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### Determining users that have been sent too many emails

The experiment is a set of users in a marketing program. We are sending campaigns to the user and want to figure out whether the user is being sent too many emails or not. Suppose you have a dataset ...
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### Parallel hyperparameter optimization techniques?

Most hyperparameter optimization technique want to evaluate points one by one. I have an expensive optimization problem, but i can run hundreds of evaluations in parallel. The dimension of the problem ...
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### MCMC for finding Bayesian Neural Network

Is someone familiar with such an approach: Suppose I want to build a bayesian neural network, with distributions over my parameters instead of point estimates. First I train my network with standard ...
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### How to test dev set on Time Series data via forecasting

I'm implementing $3$ Bayesian Deep Learning models (links below) for my masters. I'm supposed to test them on a civil engineering time series data. My models ...
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### Hidden markov model to estimate confidence in binary time series

I have binary time series representing active/inactive states eg. ...
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### Using predicted probabilities and bayesian inference to update beliefs

I'm currently working on a project to predict the likelihood of an outcome. I'd like to implement a system where the belief the event will happen is updated after running the model on new data. ...
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### Different sensitivity analysis

I have a metabolic model written in python and I would like to do a Bayesian sensitivity analysis on it to see which parameter affects it the most. Is MCMC sensitivity analysis the same as Bayesian? ...
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### Bayesian optimization for a Light GBM Model

I am able to successfully improve the performance of my XGBoost model through Bayesian optimization, but the best I can achieve through Bayesian optimization when using Light GBM (my preferred choice) ...
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### When to use BayesianSearchCV and how it works?

Can somebody highlight when to use BayesianSearchCV and how it works? I have seen the implementation of same on kaggle and wanted to explore it further. Below is the link where the implementation ...
517 views

### LSTM: Converting to Bayesian Deep Neural Network

Starting from Yarin Gal's research paper on using Dropout as a Bayesian Approximation (https://arxiv.org/pdf/1506.02142.pdf), I am trying to apply this concept to my Sequence Prediction model. My ...
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### Bayes posteriograms

My main objective is to predict the posterior probability of an individual belonging to one of the classes, using Bayes theorem. The information I have is: value of the data point mean and stdev of ...
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### How do Bayesian methods do automatic feature selection?

Someone asked me this question and I do not know I answered it correctly. I answered the question in the following way: One type of Bayesian method is Bayesian inference and feature selection has to ...
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### Estimating class prevalence in unlabelled data after predicting labels with a binary classifier

I'm looking to get an estimate of the prevalence of 1's (i.e. the rate of positive labels) in a very large dataset that I have. However, I am hoping to report this percentage as a 95% credible ...
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### Limits of using a normal distribution in Bayesian inference

When applying a Bayesian inference method such as Gaussian Process Regression (GPR), the assumption of a prior and likelihood function following a normal distribution is inherent. One can use an ...
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### Linear Discriminant Analysis + bayesian theorem = LDA classifier??

I am new to machine learning and as I learn about Linear Discriminant Analysis, I can't see how it is used as a classifier. I can understand the difference between LDA and PCA and I can see how LDA ...