# Questions tagged [bayesian]

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32 views

### Combining multiple probabilities from a classifier. Propagating probabilities

Let's say I have trained a classifier that classifies images of animals into 10 different classes. And let's say that I have 20 different images of a particular animal and because I know the ...
131 views

### High Recall but too low Precision result in imbalanced data

I was training a model using XGBoost Classifier on a heavy imbalanced database with 232:1 of binary class. Because my training data contains 750k rows and 320 features (after doing many feature ...
92 views

### 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 ...
27 views

### Update of mean and variance of weights

I'm trying to understand the Bayes by Backprop algorithm from the paper Weight Uncertainty in Neural Networks, the idea is to make a NN in which each weight has it's own probability distribution. I ...
13 views

### How does bayesian optimization with gaussian processes work?

Could someone explain in simple words what are gaussian processes how does bayesian optimization work and their combination?
833 views

### 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 (...
40 views

### Really confused with characteristics of Naive Bayes classifiers?

Naive Bayes classifiers have the following characteristics-: They are robust to isolated noise points because such points are averaged out when estimating contiditional probabilities from data. Naive ...
80 views

### How is bayesian risk computed to prune decision trees?

I've been trying to follow this paper on Bayesian Risk Pruning. I'm not very familiar with this type of pruning, but I'm wondering a few things: (1) The paper describes risk-rates to be defined per ...
49 views

### Visualize n-dimensional bayesian optimization results

I am working on a 6-dimensional bayesian optimization problem using (skopt's gp_minimize). After the optimizer ran for j iterations I would like to somehow visualize the "progress/result" of ...
5 views

### Estimating related metrics using Maximum A Posteriori

English is not my mother tongue; please excuse any errors on my part. I've recently faced a problem for which I haven't found any solutions after investing a lot of time. Here is the summarized ...
37 views

### 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 ...
219 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 ...
12 views

### When to include LDA axis on a random forest analysis

I am using the R package abcrf and have the option of including LDA (linear discriminate axis) with my other statistics. If I include these I get a different answer to when I don't, so I want to know ...
86 views

### How useful is Bayesian Inference

Last few months, I had been exposed to Bayesian Inference in ML course With further investigation, I come to place where there is MCMC technique to simulate the ...
446 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 ...
79 views

### How do Bayesian methods in machine learning help with the problem of limited data? Can this be used for image classification? [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 ...
84 views

### 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 $(X,y): \mathbb{R}^d\rightarrow \{1,2,..., K\}$ ...
17 views

### Is it a good idea to use the mean and standard deviation of coefficients from other models as my prior in Bayesian Regression?

I have a dataset that I’ve been playing around with for school I have gotten very good results with a bunch of methods (Ridge, Lasso, ElasticNet, SVM, Bagging, Stacking and NN even) Now I’m having a ...
24 views

### Does the Bayesian MAP give a probability distribution over unseen data?

I'm working my way through the Bayesian world. So far I've understood that the MLE or the MPA are point estimates, therefore ...
28 views

### Calibration curve motivation

I struggle to understand the mathematical motivation for the binary classification model calibration curve. Why do we assume that the predicted probabilities should be consistent with the proportion ...
27 views

### Bayes Classification Problem

Let we have 2 classes with their densties : p(x| w1)=$$\tfrac12e^{-|x|}$$ P(x|w2) = $$e^{-2|x-2|}$$ Where x is a one dimension feature vector . Now it is required to be known where to place the ...
21 views

### Algorithm to determine a single output value based on multiple input values [closed]

The main challenge is the lack of data. Input values come from tests results of patients. A patient takes a breath test at an interval during a timespan. The result values can range from 0 to ~200, ...
89 views

### What is the num_initial_points argument for Bayesian Optimization with Keras Tuner?

I've implemented the following code to run Keras-Tuner with Bayesian Optimization: ...
642 views

### Which tribe does Probabilistic Graphical Models fall under?

Pedro Domingos in "The Master Algorithm" listed five tribes of machine learning algorithms: Symbolists Connectionists Evolutionaries Bayesians Analogizers Which category do probabilistic ...
57 views

### Hyperparameter tuning with Bayesian-Optimization

I'm using LightGBM for the regression problem and here is my code. ...
378 views

### Changing the batch size during training

The choice of batch size is in some sense the measure of stochasticity : On one hand, smaller batch sizes make the gradient descent more stochastic, the SGD can deviate significantly from the exact ...
73 views

### 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 ...
122 views

### Why do machine learning engineers insist on training with more data than validation set?

Among my colleagues I have noticed a curious insistence on training with, say, 70% or 80% of data and validating on the remainder. The reason it is curious to me is the lack of any theoretical ...
43 views

### Practical difference between Bayesian Neural Networks and Feed Forward Neural Network with Gaussian Noise

To my understanding, a BNN's weights come from a Gaussian with trained mean and standard deviation, while a FFNN of the following form, comes from a learned weight, which acts as a 'mean', and is ...
99 views

### When to use bayesian linear regression instead of linear regression?

When does it make sense to use a bayesian approach, maybe in context to linear regression? To be more concrete: Assume you measure a certain number of devices and you wanna' check the linear ...
21 views

### I think a learning rate schedule would be counter-productive with AdaBelief. Am I wrong?

I am inclined to believe the concept of a learning rate schedule is overcome by the improvements of Adabelief over Adam. My code is on Github; please check it out and attempt to replicate my results, ...
9 views

### Tracking time-series latency using conjugate priors

I need to do a project using Bayesian statistics for a class and I am trying to apply it to my work. I help manage a time series database with 40,000+ different time series that we collect. The time ...
38 views

### Books about statistical inference [closed]

I'm currently taking a course "Introduction to Machine Learning" which covers the following topics: linear regression, overfitting, classification problems, parametric & non-parametric ...
33 views

### Bayesian Regression Model

I am new to Bayesian modeling. I am running Bayesian regression model in R using brm function from brms library, which is powered by STAN. I have a data with 10 million records. I took 10% sample out ...
114 views

### What is the difference between maximum likelihood hypothesis and maximum a posteriori hypothesis?

I am a student and I am studying machine learning. I am focusing on the concept of Bayesian learning and I have studied the maximum likelihood hypothesis and the maximum a posteriori hypothesis. I ...
98 views

### Problem understanding probabilistic generative models for classification

I am a student and I am studying machine learning. I am focusing on probabilistic generative models for classification and I am having some troubles understanding this topic. In the slide of my ...
42 views

### Hyperparameter tuning of neural networks using Bayesian Optimization

One of the assumptions for finding good hyperparameters using Bayesian optimization (GP) is that the unknown function is smooth. Is this assumption valid for neural networks or at least for most of ...
45 views

I'm working with a dataset $X$ (of length $N$) of count data, which looks like: I developed a statistical model which can be improved, so I'm asking for any suggestions, for instance, differnet ...
152 views

### What makes the posterior intractable?

In the setting of Variational AutoEncoders, i.e. when we want to find the posterior distribution over the data generating, latent variable z, given some ...
39 views

### Custom Loss Function for Mixing Sparse and Dense Features for a Prediction Problem

I have a largely uncorrelated feature space of about 40 dichotomous features, using which I'm trying to predict a continuous target variable. Now, some of these features are very sparse (Active less ...
49 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?
22 views

### Improve confidence interval accuracy

I am doing a linear regression on log-transformed data and I use the bayesian approach to model the predictive distribution and construct my 90% prediction Interval. The problem with this approach is ...
127 views

### BPR TripletLoss Recommender System

I am trying to modify the code of this repo to build a recommender system based on BPR triplet loss. In particular I modified the TripletLoss layer class like this ...
155 views

### Mean estimation for nested location data

I want to estimate the average income for a location. I have nested data in the following way: A block is inside a neighborhood, which is inside a zipcode, which is inside a district, which is inside ...
2k views

### Opinions on an LSTM hyper-parameter tuning process I am using

I am training an LSTM to predict a price chart. I am using Bayesian optimization to speed things slightly since I have a large number of hyperparameters and only my CPU as a resource. Making 100 ...
29 views

### 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 ...
65 views

### Classification of OLS regression coefficients

A variable $A$ (reaction time) is log-normally distributed, i.e. $\log(A) \sim \mathcal{N}(0,\sigma^2)$ and is linearly dependent of $n$ variables $X = (X_1,\ldots,X_n)$, i.e. \begin{align} A &= \...
81 views

### Firebase AB testing algorithm

We have run an AB test at firebase which has the following results: I was also building my own Bayesian AB-test suite and was wondering how they came to these conclusions. What I was doing was ...