Questions tagged [bayesian]

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52 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 ...
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0answers
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 ...
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45 views

Poisson model with overdisperssion

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 ...
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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 ...
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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 ...
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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 ...
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0answers
39 views

Specifying priors in rstanarm for hierarchical model

We are given the model $$ \begin{align*} y_{ij} & \sim \mathsf{Normal}(\alpha_j + \beta x_i, \sigma^2)\\ \alpha_j & \sim \mathsf{Normal}(\gamma_0 + \gamma_1 u_j, \tau^2) \end{align*} $$ with ...
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31 views

GMM with Dirichlet prior

I'm learning about the variational inference - mean field approximation on from this online course DeepBayes2019 page 30 The probabilistic model is written as follows: $p(X, Z \mid \pi, \mu, \lambda) =...
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27 views

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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0answers
91 views

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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0answers
1k views

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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0answers
34 views

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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0answers
70 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 ...
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1answer
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 ...
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1answer
80 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 ...
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44 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 ...
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0answers
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 ...
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0answers
716 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/...
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0answers
127 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 ...
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0answers
155 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 ...
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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 ...
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0answers
199 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: ...
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2answers
861 views

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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1answer
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?
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1answer
91 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: ...
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2answers
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 ...
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1answer
154 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 ...
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1answer
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 ...
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1answer
22 views

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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1answer
99 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 ...
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2answers
258 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 ...
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2answers
329 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 ...
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1answer
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 ...
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1answer
127 views

Understanding Bernoulli Trials, Bayesian Setting

I am required to complete a project on ML applications. I guess there is a lot of statistics in ML, not helpful for a non-maths background. I am getting too bogged down by notations. There are too ...
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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 ...
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0answers
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 ...
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1answer
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 ...
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0answers
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 ...
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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 ...
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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 ...
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1answer
58 views

Hyperparameter tuning with Bayesian-Optimization

I'm using LightGBM for the regression problem and here is my code. ...
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0answers
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, ...
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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 ...
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0answers
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 ...
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1answer
128 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 ...
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0answers
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 &= \...
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1answer
247 views

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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1answer
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 ...
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1answer
220 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 ...
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69 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 ...

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