# 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.

145 questions
Filter by
Sorted by
Tagged with
16 views

### This is a Proposed course

The following is a proposed class exercise for a course introducing automated data-driven decision-making to new engineers, given their interest in the topic. I'm interested in feedback. Does the ...
10 views

### Identifying Underperforming Movies Using Bayesian Metrics or Time Series Approach?

I have two years' worth of movie rental sales data. I'm interested in identifying underperforming movies based on these variables. So far, I'm considering creating a Bayesian product metric that will ...
• 1
5 views

### why do junction trees need to be without cycles?

I am studying the junction tree message passing algorithm for bayesian inference and do not understand why junction trees need to be without cycles. All I know is that it is needed for "efficient ...
1 vote
59 views

### Is there a model that can predict continuous data while also providing a level of confidence in the prediction?

The problem with Bayesian neural network seems to be that it is primarily working for classification problems. Is it possible to adjust this neural network, or even use a different model if one exists,...
• 13
1 vote
106 views

### How do I work with time-series data of temperature?

So I have some equipment temperature and i have outside temperature (both are collected daily) and I want to predict the equipment temperature. However, I'm new to this and unsure about which model to ...
• 11
1 vote
10 views

### Which statistical approach is best for diverse conversion rates in a controlled experiment?

Our software startup builds chat bots for ecommerce websites. The chatbot talks to customers that open the chat bot, and has the goal of closing the sale with the store’s main product. We have about ...
• 111
13 views

### Can we calculate Bayes Error rate, if we have a simulated data?

I am going through ISL(Python) and in section 2.2.3 ( Page No. 36), the author writes, "For our simulated data, the Bayes error is 0.133. It is greater than zero, because the classes overlap in ...
8 views

### Global variant of Jeffreys Prior

The Jeffreys prior can be viewed as a way to ensure that "accidental similarities" between nearby models in some hypothesis space do not produce commensurate "accidental biases" in ...
• 101
6 views

### Updating Model Parameter in Domain Adaptation with Additional Features

I have developed a model where the parameter is initially calibrated using common features (X1, X2, X3) from both source and target domains. Now, I want to incorporate additional specific features (X4 ...
• 103
20 views

### Intuition behind this bayesian probability?

Original Question - Prevalence of a disease X is 0.1%. You take a test for this disease and it turns out positive. This test is 99% accurate. What is the probability of you having the disease given ...
23 views

### Why posterior in EM algorithm tractable but in VAE not?

So I've read through this post, including the Bayesian Mixture of Gaussians supplied in the link in the last comment by oW_ there, to really see why the posterior is intractable. I just don't see yet ...
• 101
20 views

### $p(f|X,Y) \propto p(Y|X,f)p(f)$

I'm reading a paper where it mentions Gaussian processes, the author defines a prior distribution over function space $p(f)$ and states the following about the posterior $p(f|X,Y) \propto p(Y|X,f)p(f)$...
• 61
1 vote
91 views

### CDF/PDF vs Monte Carlo

I’m a developer reading the book Bayesian statistics the fun way In chapter 15 they use hypothesis testing using Monte Carlo simulation to pick random values from two intercepting beta distributions I ...
1 vote
47 views

### Occam's factor and the VC dimension

I was watching this lecture by Prof. Dr. Philipp Hennig (Probabilistic ML) and when he reached this formula which is the type two maximum log likelihood I had the following question: The Occam's ...
31 views

### Bayesian Linear regression for non-gaussian distribution

Which distribution should be considered for non-gaussian distribution while building Bayesian Linear regression. I am trying to build this model using Pymc3 library but since I have just started to ...
20 views

### Parameter outside specified range using HGboost/Hyperopt library

I am trying to use the HGboost library which uses the Hyperopt library for doing hyperparameter optimization of an XGboost model. The script runs fine but the optimized parameter for "...
• 101
123 views

### Prediction intervals for future timestamps - out-of-sample

I've created a model for out-of-sample forecasting that uses multistep recursive strategy to reduce my problem to regression, the predictions are sufficient but I was wondering if there is any ...
54 views

### Using Bayesian statistics in time series forecasting

I would like to forecast demand count time series of taxi fleets at different locations on the map at different points in time. I.e. multivariate demand Time series forecasting. Given hierarchinal ...
78 views

### Is there no reason to ever use naive bayesian learning?

I found this slide in the university course on machine learning I am currently taking. The reasons seem sound but I have not found this confirmed anywhere everywhere I read that for certain types of ...
1 vote
84 views

### Model uncertainty quantification

I'm reading a paper about model uncertainty quantification. Specifically, it says epistemic uncertainty is a kind of uncertainty due to lack of knowledge about a particular region in the input space. ...
• 61
55 views

1 vote
67 views

### bayesian neural network and the flipout estimator

i was reading the flipout paper and i stumbled over this passage. I will summarize the main point here: In bayesian neural networks the weights are random variables sampled from a distribution. W is ...
• 111
41 views

### Does a classifier based on optimal bayes classifier equation classify every new instance the same way?

I'm trying to understand how optimal bayes classifier works and I was wondering if, given that the function we try to maximize when making a new prediction does not depend on the instance we are ...
63 views

### Bayesian linear regression using pymc

Assume three features: $x_1,x_2,x_3,$ and a continuous label $y.$ I want to use pymc in python to fit a ...
• 127
14 views

### Adressing uncertainty of a spatio-temporal multivariate timeseries with random temporal gaps

Imagine there are multiple locations of interest from where water samples are gathered manually. Each sample is immediately analyzed, converted to a numerical value (a real number) and fed into a ...
58 views

### How Naive Bayes makes prediction based on scikit-learn?

I need to understand, how multinomial-naive-bayes can make prediction based on scikit-learn implementation. I saw the source code but I want to understand the math behind it. Could you please explain ...
1 vote
125 views

### How can I deal with a computationally expensive simulator method in Sequential Monte Carlo/Approximate Bayesian Computation?

I am doing Approximate Bayesian Computation with Sequential Monte Carlo with PyMC in a way that is similar to what is described in this example of the ...
• 11
293 views

### Understanding the uncertainty in gaussian processes

Consider the following image: which is an fitted GP. Note how $0 <= x <= 2$ yield a much higher uncertainty than e.g $5 <= x <= 8$. Thus gps are good when dealing with the exploration vs ...
41 views

### Force predictions of 2 time series models with different time steps to be consistent

Suppose I have a time series. Let's say it is of the number of sales in a shop. Suppose I am looking to make two models - model 1 which predicts future values by weekly time steps (total sales per ...
• 1
115 views

### Practical example of difference between p(y|x) and p(x|y)

I've been reading about the difference between generative models and discriminative models. I know that for generative models we learn the joint probability p(x,y) or just p(x|y) and p(y). For a new ...
• 93
42 views

### What is the Bayesian distribution of two p.d.f's?

I whant to make this: Theese are the inputs I'm traying to replicate this for my app, but don't know how its done. As you can see, there must be a preset value for the sigma with the inputs of type ...
1 vote
101 views

### Probability for Nth Place in Race from Bradley-Terry Model Inputs and Outputs

I have created a motorcycle race prediction model that is given pairs of racers and outputs the probability of each rider beating the other in each pairwise comparison. That info is then processed ...
32 views

### Bayesian state description in Reinforcement Learning

What's the best approach to feed a bayesian description of an observed state to a Reinforcement Learning agent? Brief context: I have an agent situated in an environment, which it perceives through a ...
28 views

### Why aren't Bayesian models straightforwardly superior to all/most other models if they model the real-world better?

Why aren't Bayesian models straightforwardly superior to all/most other models if they model the real-world better? As possibly displayed by the following chart: source
• 416
17 views

### How "much" should belief be weighted against numerical measures of accuracy/correctness?

How "much" should belief be weighted against numerical measures of accuracy/correctness? It's possible to devise numerically low-quality models, but that have very good empirical validity, ...
• 416
1 vote
49 views

### How to guide exploration in reinforcement leanring/model predictive control/dual control problem

Consider the following optimization/control problem: We aim to maximize the cumulative reward R during the horizon H by every day allocating a portion of total budget B to our two different investment ...
27 views

### How calculate probability when we have continuous features?

Suppose that we have a dataset with four features and each feature follows different distribution (normal,beta,gamma...). All features are continuous. So, how we can calculate the probability of any ...
1 vote
26 views

### Problem understanding the forward algorithm for HMMs

I found a recursive version of the forward algorithm on wikipedia, however I don't understand the notation given in the pseudocode: What means $$x_{t-1}$$ under the summation sign? What do I need to ...
• 131
1 vote
23 views

### What methods I could use to analyze the contingency table?

I am data science beginner, and I have a question about methods that I could use to analyze the following data. It is a simple case, I am trying to check the influence of cohabitation before marriage ...
• 11
67 views

### What are the requirements for a word list to be used for Bayesian inference?

Intro I need an input file of 5 letter English words to train my Bayesian model to infer the stochastic dependency between each position. For instance, is the probability of a letter at the position 5 ...
• 103
1 vote
94 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 ...
• 111
368 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 ...
• 161
1 vote
12 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 ...
• 161
1 vote
68 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. ...
1 vote
26 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 ...
• 133
32 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 \$...
66 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 ...
1 vote
849 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 ...
• 224