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.

Filter by
Sorted by
Tagged with
1 vote
1 answer
43 views

Incorrect example of applying Bayes theorem

I have been reading the book "The Data Science Design Manual" (by Steven S. Skiena) and I came across an example that explained how the Bayes theorem can be applied that confused me and made ...
user avatar
  • 111
1 vote
0 answers
34 views

Why and how Variational Inference underestimates variance?

I referred to the Quora link here as well, but could not understand clearly. Can anyone please help me understand why and how variational inference underestimates the variance of the true posterior ...
user avatar
1 vote
0 answers
9 views

Confusion regarding which distribution Monte Carlo considers for sampling

Considering Bayesian posterior inference, which distribution does Monte Carlo sampling take samples from: posterior or prior? Posterior is intractable because the denominator (evidence) is an ...
user avatar
0 votes
0 answers
5 views

Model transfer with limit to none label information

I have this problem I hope to get some help here. Say I have a type of product A whose measurements are X_A and an outcome property is ...
user avatar
  • 21
0 votes
0 answers
10 views

How to test if a curve is well described by an ellipse?

I have a set of data points in 2D, and I am trying to come up with some sort of statistical to determine if the points fall along an ellipse. My idea so far is to fit an ellipse to the points, take ...
user avatar
  • 1
1 vote
1 answer
17 views

Confused on Naive Bayes classifier

In the last part of Andrew Ng's lectures about Gaussian Discriminant Analysis and Naive Bayes Classifier, I am confused as to how Andrew Ng derived $(2^n) - 1$ features for Naive Bayes Classifier. ...
user avatar
0 votes
0 answers
12 views

Hierarchical Bayesian model

Are Multinomial Naive bayes, Multi-variate Bernoulli Naive Bayes, and semi naive bayes, considered to be Hierarchical Bayesian models? Much thanks
user avatar
  • 1
1 vote
1 answer
18 views

What thought processes do people use for generating priors on a variable's probability distribution?

Example Consider a block of text with a variety of sentence types within it, of which there are 7. Within a text these sentences will be more or less likely to appear, dependent on where in the text ...
user avatar
  • 133
2 votes
0 answers
10 views

Probability distributions for Directed Cyclic Graphs

Given a directed cyclic graph where vertex A is 'infected', and there are different infection probabilities between each node, what is the best approach towards computing the conditional probability $...
user avatar
0 votes
0 answers
7 views

Hidden Markov Model with control input?

Given that HMMs and Kalman Filters are both state space models with recursive aspects and KFs allow for control inputs to influence the prediction at each step, I'm wondering if any similar methods ...
user avatar
0 votes
0 answers
9 views

Linked Bayes Boxes

(You might think that this is more a more appropriate question for MathEd, but they tell me that it's more appropriate here, so go figure...) I'm trying to use linked Bayes Boxes in a spreadsheet to ...
user avatar
0 votes
1 answer
13 views

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 ...
user avatar
  • 1
1 vote
1 answer
29 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 ...
user avatar
1 vote
1 answer
23 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 ? ...
user avatar
0 votes
0 answers
26 views

Lasso (or Ridge) vs Bayesian MAP

This is the first time I have posted here. I am looking for some feedback or perspective on this question. To make it simple, let's just talk about linear models. We know the MLE solution for the $l_1$...
user avatar
1 vote
0 answers
7 views

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 ...
user avatar
  • 23
0 votes
0 answers
18 views

Expectation of ELBO in Variational Autoencoder

I am working with VAEs. My input is x, which is a product of two variables $x_1$ and $x_2$. The objective (ELBO) of VAE in terms of x is: $E_{z\sim Q}[\log P(x|z)] - \mathcal{D}[Q(z|x)||P(z)]$. I want ...
user avatar
0 votes
0 answers
17 views

How to predict categories without class labels using Bayesian methods?

Suppose I have the following financial data. I have to output a column with its result that shows what type of categories each row belongs. It can be an income, an expense, or a Capex (Capital ...
user avatar
2 votes
1 answer
85 views

Bayes theorem on the probability of an object drawn at random using percentages (not Naive Bayes)

It's the normal Bayes equation but I'm not sure if I've calculated this correctly or how to check my work, here is a somewhat similar question but I wasn't sure if our math was the same, the question ...
user avatar
0 votes
0 answers
27 views

Multivariate testing

I'm going to run a test with 4 different variants (3 variants and a control group), and we want to find the variant with the highest conversion. Are there any resources/methods in R/python to: ...
user avatar
  • 5,594
0 votes
0 answers
12 views

Inference over a fixed term - what analysis am I doing here?

I have a fixed term of, say, one year. At the end of the term there is an observation of true / false, say a customer either renews or cancels their subscription. This decision is probably based on ...
user avatar
0 votes
0 answers
16 views

lack of consistency in Bayesian optimization of xgboost's hyperparameters

I am trying to optimize the hyperparameters in an xgboost model using Bayesian optimization and the mlrmbo R package. The simplified code below seem to produce reasonable results, but the problem I ...
user avatar
  • 1
0 votes
0 answers
14 views

Bayesian interference - interpretation of results of two groups

I'm trying to understand Bayesian interference on example the comparison of two groups from this source. It's not all too clear about interpretation of some parameters in summary, like: mcse_mean ...
user avatar
  • 1,339
2 votes
1 answer
211 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 ...
user avatar
0 votes
1 answer
55 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?
user avatar
  • 480
0 votes
0 answers
6 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 ...
user avatar
  • 1
0 votes
0 answers
14 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 ...
user avatar
3 votes
2 answers
175 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 ...
user avatar
0 votes
1 answer
66 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 ...
user avatar
3 votes
2 answers
727 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 ...
user avatar
  • 63
0 votes
0 answers
18 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 ...
user avatar
  • 123
0 votes
0 answers
40 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 ...
user avatar
4 votes
1 answer
33 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 ...
user avatar
1 vote
0 answers
25 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, ...
user avatar
  • 111
1 vote
1 answer
38 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 ...
user avatar
0 votes
1 answer
230 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: ...
user avatar
0 votes
1 answer
120 views

Hyperparameter tuning with Bayesian-Optimization

I'm using LightGBM for the regression problem and here is my code. ...
user avatar
  • 3
6 votes
1 answer
904 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 ...
user avatar
4 votes
1 answer
98 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 ...
user avatar
  • 41
1 vote
1 answer
113 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 ...
user avatar
  • 11
3 votes
1 answer
130 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 ...
user avatar
1 vote
0 answers
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 ...
user avatar
0 votes
2 answers
61 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 ...
user avatar
2 votes
1 answer
248 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 ...
user avatar
  • 721
2 votes
1 answer
117 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 ...
user avatar
  • 721
1 vote
1 answer
55 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 ...
user avatar
  • 1,588
0 votes
1 answer
408 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 ...
user avatar
  • 125
3 votes
1 answer
147 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 ...
user avatar
  • 480
1 vote
0 answers
46 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 ...
user avatar
0 votes
1 answer
236 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 ...
user avatar
  • 313