# Questions tagged [bayesian]

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### Effect of outliers on Naive Bayes

Are Naive Bayes algorithms affected by outliers in the data? Suppose there is a data set, does one need to remove outliers before applying Naive Bayes?
49 views

### PyMC3: how to efficiently regress on many variables?

I am sorry ahead of time if this seems like a basic question, but I had difficulty finding resources online addressing this. In PyMC3, when building a basic model of a few variables, it is easy to ...
48 views

### Bayesian Neural net with non probibalistic Data?

Is it possible to construct a Bayesian Neural Network without Probability Distributions as dependent Variable for purpose of predictive modeling? I mean, if id like to Infer on a Specific Value, like ...
105 views

### Bayesian optimisation in deeplearning

Has anyone tried using Bayesian optimisation to get best learning rates, and other hyperparameters for deeplearning. How to change the parameters between the training. Any examples on callbacks? Can ...
4k views

### What makes a Tree-Structured Parzen Estimator “tree-structured?”

From what I understand the Tree-Structured Parzen Estimator (TPE) creates two probability models based on hyperparameters that exceed the performance of some threshold and hyperparameters that don't. ...
384 views

### How to do hidden variable learning in Bayesian Network with Python?

I learned how to use libpgm in general for Bayesian inference and learning, but I do not understand if I can use it for learning with hidden variable. More precisely, I am trying to implement approach ...
62 views

### How to compute the maximum likelihood hypothesis?

The Bayes theorem states that: \begin{equation} P(h|D) = \frac{P(D|h)P(h)}{P(D)} \end{equation} where $D$ is the dataset and $h$ is an hypothesis from the hypothesis space $H$. Now (I'm not sure so ...
162 views

### 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 ...
53 views

### 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 ...
656 views

### 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 ...
48 views

### Probability of event given two depandant events [closed]

Is there anyway, to compute the probability of the event given two dependent events? I know that Bayes can help if those events are independent, but what if condition events are dependent?
266 views

### How to best estimate the coefficients of a confusion matrix in case of strong class imbalance?

I have a trained binary classifier (forget about how this was trained and think of it as a magical black box) and I would like to measure its classification performance (e.g. compute a confusion ...
52 views

### Where can we find the application of bayes's theorem in Bayesian optimiation with gaussian processing

I am trying to learn bayesian optimisation by following this tutorial. However, until now I don't get the relation between bayes's theorem to the gaussian process formalism. Any ideas?
39 views

### When to model a problem by using the Bayes' theorem?

I have a labeled training dataset where each observation has a sentence either in English or in French as its predictors and its label (target value) is whether this sentence is English or French. The ...
65 views

### How Do Bayesian Methods in Machine Learning Help With the Problem of Limited Data? Can This Be Used for Image Classification/Recognition? [closed]

When reading about machine learning, I've often come across information stating that Bayesian methods in machine learning are effective when you only possess a limited amount of data. As someone who ...
25 views

### Algorithms behind “smartlinks” for online traffic distribution

Several traffic networks offer "smart links", where you can send online traffic, and the traffic will be sent to the most profitable segment. These networks include Monetizer, Glize etc. I.e. if you ...
689 views

### Correct calculation of BIC (Bayesian Information Criterion) to determine K for K-Means

I am trying to calculate BIC in python. In python, there is no inbuilt library for computing BIC. I referenced the following link to compute variance and BIC further:- https://stats.stackexchange.com/...
110 views

### How to integrate out hyperparameters of Gaussian process for Bayesian optimization?

I read this paper (https://arxiv.org/pdf/1206.2944.pdf) discussing about practical issues of Bayesian optimization and they mentioned that integrating out hyperparameters of Gaussian process using ...
87 views

### How to calculate the Probability for the Unconditional Node in the Bayesian Belief Network?

In the popular example for Bayesian Belief network Burglary Alarm how is the probability for burglary P(B) and earthquake P(E) calculated as 0.001 and 0.002 respectively? Is it an assumption made or ...
224 views

### Statistical inference on a very small datasets

I have been working with machine learning for about a year now, but mostly with large datasets. However, I am currently working on a problem with a very small dataset. Here is my problem: I am ...
134 views

### Custom regularisation for logistics regression

My understanding of l2 regularisation: Weights of the model are assumed to have a prior guassian distribution centered around 0. Then MAP estimate over data adds an extra penalty in cost function. My ...
893 views

### Creating a posterior distribution for classic coin flipping in python using grid search

I'm reading the book "Bayesian Analysis with Python" and the author provides some python code designed to show the grid search method of obtaining an approximate posterior distribution for the classic ...
27 views

### How to interpret long equations in Deep Learning papers

For eg. I've been studying a paper on Recommender systems using collaborative deep learning and I've just started learning. The paper revolves around the NN representation as shown below The ...
383 views

### Bayesian linear regression / categorical variable / Laplace prior

I'm trying to do feature selection in the bayesian framework with a Laplace prior with the following code in Python; Code: ...
67 views

### RNADE vs MDR vs BART vs Bayesian Linear Regression

I'm looking at a collection of problems where I need to forecast the probability of a continuous variable. My dependent variables are a combination of categorical and continuous. It looks like there ...
73 views

### ML algorithm where variable importance depends upon other variables - specifying conditionality

I am designing an algorithm that can detect cheating in a game of chess. I have a database of players who have been flagged by moderators as cheating, along with a number of game-level ...
355 views

### Replacing missing value by class conditional mean

I have two classes, $p(x|y=0)$ and $p(x|y=1)$ with ${{\mu }_{0}}$ and ${{\mu }_{1}}$ as mean and shared covariance matrix $\Sigma$. Now, I have a missing feature ${{x}_{n}}$ for a particular ...
185 views

### How to use pymc3 to sample the mean of a Pareto random variable?

I Have a variable which is Pareto-ly distributed 'x', with unknown alpha and m. I want to find out the distribution of its mean, so I use the following model: ...
52 views

### information leakage when using empirical Bayesian to generate a predictor

Consider the following problem: I want to predict the next bat of a set of baseball player. I have a training data set, where it contains the historical bat records (0-1 encoded, which is our target ...
275 views

### Ensemble learning [closed]

I am currently working to build a mathematical model to predict the stock market. I learned that the best way to do such thing is no longer to make one big best model, but rather to gather several ...
1k views

### Why the estimated Lasso coefficients of almost all variables are equal to zero?

I would greatly appreciate if you could guide me. In fact, I used "Bayesian Optimization " to tune hyper-parameters of Lasso but the estimated Lasso coefficients of almost all variables are equal to ...
471 views

### Implement gaussian mixture model with stochastic variational inference

I am trying to implement Gaussian Mixture model with stochastic variational inference, following this paper. This is the pgm of Gaussian Mixture. According to the paper, the full algorithm of ...
36 views

### Finding the most important questions from a questionary-> results

Lets assume that I have 5000 articles and I create the TF-IDF of these articles. Now I ask som people to answer 30 questions and I create the TF-IDF of these answers from the IDF of the articles and ...
1k views

### Modeling uncertainty from Logistic Regression

Logistic regression is a part in a simulation pipeline that I use for some scenario analysis. The dataset that this is based on is not small but relatively noisy, and only one explanatory variable/...
103 views

### Is deep learning a must in a Data Science MSc programme? [closed]

I am reading the programme outline of this two-year MSc in Data Science and I found that it has no deep learning content (as in many other european ones). I am no expert but as far as I've seen I ...
2k views

### generalized likelihood ratio test (GLRT)

I am having trouble in understanding the generalized likelihood ratio test (GLRT). Can anyone explain what it is to me, or point me toward an easy-to-understand reference? Is it a supervised or ...