# Questions tagged [bayesian]

Bayesian statistics is a statistical paradigm that contrasts with that of frequentist statistics. Bayesian methods rely on prior information do determine the degree of belief in the probability of a value.

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### Bayesian Linear regression for non-gaussian distribution

Which distribution should be considered for non-gaussian distribution while building Bayesian Linear regression. I am trying to build this model using Pymc3 library but since I have just started to ...
6 views

### Parameter outside specified range using HGboost/Hyperopt library

I am trying to use the HGboost library which uses the Hyperopt library for doing hyperparameter optimization of an XGboost model. The script runs fine but the optimized parameter for "...
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### Prediction intervals for future timestamps - out-of-sample

I've created a model for out-of-sample forecasting that uses multistep recursive strategy to reduce my problem to regression, the predictions are sufficient but I was wondering if there is any ...
34 views

### Using Bayesian statistics in time series forecasting

I would like to forecast demand count time series of taxi fleets at different locations on the map at different points in time. I.e. multivariate demand Time series forecasting. Given hierarchinal ...
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### Is there no reason to ever use naive bayesian learning?

I found this slide in the university course on machine learning I am currently taking. The reasons seem sound but I have not found this confirmed anywhere everywhere I read that for certain types of ...
1 vote
74 views

### Model uncertainty quantification

I'm reading a paper about model uncertainty quantification. Specifically, it says epistemic uncertainty is a kind of uncertainty due to lack of knowledge about a particular region in the input space. ...
6 views

### How to interpet the bolded lines in bayesopt

I am using bayesopt to maximize a function that is everywhere less or equal to zero $(f(x) \leq 0)$. The score is essentially a negated Mean Absolute Error because the default behavior of bayesopt is ...
32 views

1 vote
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### bayesian neural network and the flipout estimator

i was reading the flipout paper and i stumbled over this passage. I will summarize the main point here: In bayesian neural networks the weights are random variables sampled from a distribution. W is ...
32 views

### Does a classifier based on optimal bayes classifier equation classify every new instance the same way?

I'm trying to understand how optimal bayes classifier works and I was wondering if, given that the function we try to maximize when making a new prediction does not depend on the instance we are ...
12 views

### Understanding the calculations of bayesian classification rule given features with known distribution

I have some difficulties using the Bayes rule and the calculations used for estimation of a given class in a classification task if we know the distribution (and its parameters) of the feature vector: ...
37 views

### Bayesian linear regression using pymc

Assume three features: $x_1,x_2,x_3,$ and a continuous label $y.$ I want to use pymc in python to fit a ...
14 views

### Adressing uncertainty of a spatio-temporal multivariate timeseries with random temporal gaps

Imagine there are multiple locations of interest from where water samples are gathered manually. Each sample is immediately analyzed, converted to a numerical value (a real number) and fed into a ...
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### Sklearn Dirichlet Mixture

I'm using sklearn.mixture.BayesianGaussianMixture to fit a Dirichlet Process Mixture Model, and my data is 64 dimensional and have sample size 10000. However, the ...
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### Techniques for choosing datasets from pool of datasets to create priors(time-series data, DTW? spike and slab?)

Let us consider a scenario where we have a pool of 100 datasets from various customers, with varying sizes containing sales and budgeting(mutiple channels) data per date. The datasets can range from ...
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### how do i compute the predictive covariance matrix from the posterior samples?

I have generated with EMCEE some posterior samples from a statistical model whose likelihood is a multivariate gaussian. it's a regression problem. can you explain me how I can use these samples to ...
35 views

### How Naive Bayes makes prediction based on scikit-learn?

I need to understand, how multinomial-naive-bayes can make prediction based on scikit-learn implementation. I saw the source code but I want to understand the math behind it. Could you please explain ...
13 views

### find the parameter that minimize a multivariate distribution

i was trying to use scipy's minimize scalar to find the value of the parameter T that minimize the negative log-likelihood of a multivariate distribution with covariance matrix C. if i understood ...
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### Framing a probabilistic time dependent problem

I need help framing the following problem: I have a dataset where I know for each day, at customer level, that someone with device X bought device Y. Example: At day 1 50 people with device X bought ...
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### Combine prior knowledge probability with ML model probability results

I want to combine prior knowledge to improve my machine learning model. According to Bayes rule, max posterior is obtained when multiply prior with MLE. I multiply prior probability with the machine ...
1 vote
658 views

### Markov Process and transition matrix

I would like to find some good courses but also a quick response on how to model transition matrix given the states. Imagine having 4 states and the following array [1,2,4,1,3,4,2 etc etc]. What ...
196 views

### Are there any implementations of non Naive Bayes Classifier in Python?

Naive Bayes assumes that predictors are independent. Though this assumption is quite powerful, in some scenarios it fails miserably . So are there any implementations of non Naive Bayes in Python ? ...
1 vote
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### Bayesian Linear Regression using the Kernel Trick vs Constructing features using Kernels as Prototypes

How different is it to do Bayesian linear regression using the GP approach (kernel trick) versus constructing features using kernels to prototypes? As far as I know, this very basic question is ...