Questions tagged [bayesian]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
1
vote
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 ...
0
votes
1answer
73 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 ...
0
votes
2answers
25 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 ...
2
votes
2answers
222 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 (...
0
votes
0answers
14 views

Hypothesis Space of Bayes' Optimal Classifier?

What is the hypothesis space for an Optimal Bayes' Classifier and why do the assumptions of a Naive Bayes' Classifier (that features are conditionally independent of each other) narrow the search ...
0
votes
0answers
31 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 ...
0
votes
1answer
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 ...
0
votes
0answers
26 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 ...
1
vote
1answer
35 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 ...
1
vote
2answers
61 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 ...
1
vote
1answer
84 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 ...
0
votes
1answer
32 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 ...
1
vote
1answer
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 ...
1
vote
0answers
43 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 ...
0
votes
1answer
34 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 ...
3
votes
1answer
81 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 $(X,y): \mathbb{R}^d\rightarrow \{1,2,..., K\}$ ...
0
votes
1answer
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 ...
2
votes
0answers
61 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 ...
1
vote
0answers
25 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 ...
1
vote
1answer
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?
1
vote
0answers
17 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 ...
0
votes
1answer
55 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 ...
0
votes
0answers
10 views

Machine learning. Input: set of distributions, Output: distribution

I have a set of features where typical machine learning techniques do not work very good. All features have very different distributions, some heteroscedasticity is also present. The distribution ...
1
vote
0answers
18 views

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 ...
4
votes
2answers
154 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 ...
1
vote
2answers
530 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 ...
2
votes
1answer
23 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 ...
0
votes
0answers
38 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 &= \...
1
vote
0answers
61 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 ...
1
vote
1answer
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 ...
0
votes
0answers
8 views

what does “Tree” refer to in Tree-structured Parzen Estimators

I am going through the literature of Hyperparameter optimization techniques and came across TPE. There is very little to no explanation on why the name has "Tree" in it. What is Tree referring to? and ...
3
votes
1answer
860 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 ...
0
votes
0answers
34 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 ...
2
votes
1answer
87 views

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. ...
4
votes
1answer
37 views

Bayesian regularization vs dropout for basic ann

Does it make sense conceptually to apply dropout to an artificial neutral network while also applying bayesian regularization? On one hand I would think that technically this should work just fine, ...
2
votes
1answer
17 views

How to calculate probability of non independence using bayes theorem?

i looked into one of the post about naive bayes calulation of naive part Predit the class label for instance (A=1,B=2,C=2) using naive Bayes classifcation. Let C1 be class 1 and C2 be class 2. For ...
2
votes
1answer
72 views

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 ...
0
votes
0answers
29 views

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 ...
3
votes
0answers
31 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 ...
4
votes
2answers
1k views

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?
1
vote
0answers
29 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) =...
3
votes
3answers
278 views

What is the meaning of likelihood?

I am studying Bayes probability applied to machine learning, and I have encoutered the concept of likelihood, which I don't understand. I have seen that the Bayes rule is: $P(A|B)=\frac{P(B|A)P(A)}{...
0
votes
1answer
171 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 ...
0
votes
0answers
16 views

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 ...
0
votes
0answers
12 views

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: ...
2
votes
0answers
106 views

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 ...
4
votes
2answers
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 ...
2
votes
1answer
115 views

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 ...
0
votes
0answers
25 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 ...
1
vote
0answers
80 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, ...