Share Your Experience: Take the 2024 Developer Survey

# Questions tagged [probability]

Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur or how likely it is that a proposition is true.

107 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
4k views

### Loss Function for Probability Regression

I am trying to predict a probability with a neural network, but having trouble figuring out which loss function is best. Cross entropy was my first thought, but other resources always talk about it in ...
• 191
675 views

### Analysis of probability distribution of each features and Machine Learning

While I know that probability distributions are for hypothesis testing, confidence level constructions, etc. They definitely have many roles in statistical analysis. However, it is not obvious to me ...
• 411
171 views

### Relation between an underlying function and the underlying probability distribition function of data

I heard and read a lot of times the following statements and got a lot of confusion over time. Statement 1: The goal of machine learning is to get a function from the given data Statement 2: The goal ...
• 157
534 views

### how can i plot probability distribution of my classes in the way below?

All, I would like to plot the following: I have a binary classification problem where I am using xgboost as my 'model' below: ...
• 526
453 views

### What is the difference between KL-divergence, JS-divergence, Wasserstein distance and MMD?

I was reading about different distribution distances, and came across Kullback-Leibler divergence Jensen-Shannon divergence Wasserstein distance Maximum mean discrepancy (MMD) The book was too ...
• 143
47 views

### How to make machine learning model that reports ambiguity of the input?

Suppose I want to build a neural network regression model that takes one input and return one output. Here's the training data: ...
• 305
71 views

### Marginalization of joint distribution

I am trying to understand how you marginalise a joint distribution. In my case I have a fair coin, $P(C) = \frac12$ and a fair dice $P(D) = \frac16$. I am told I win a prize if I flip the coin and ...
• 31
546 views

### Threshold tuning with one-vs-rest for multi classification python

I’m currently using a One vs Rest Random forest algorithm for multi class classification problem using Python, and I want to find the optimal threshold for each class, How can I do this with OVR (One-...
• 21
95 views

### What algorithms can handle probabilistic targets?

I have a classification problem where I want to want to use probabilities instead of classes to train my model to learn to output probabilities. In my dataset, I have instances where the probabilities ...
• 139
37 views

### overlapping of surfaces with holes

I have two disks, of equal diameter. Both disks have a 'spatter' of holes, in random positions (from a two-dimensional - i.e., surface - uniform distribution; in other words, holes close to the sides ...
17 views

### Connection between prob output LogisticReg/SVM and ROC

I have the following ROC generated using LPOCV and Logistic regression or SVM (l2 norm). Now, let's say I have a test set containing 10 patients and I get that the probabilities of those patients to ...
162 views

### Difference between Gibbs sampling and variational Bayes inference

After reading blogs and books, I came to the conclusion that Gibbs sampling and variation Bayes are methods for estimating or inference of posterior. The below link is described but it's difficult to ...
40 views

### Wavenet joint probability

As presented in the first article of Google Wavenet (https://arxiv.org/pdf/1609.03499.pdf) the model can approximate the joint probability of the whole sequence (raw audio waveform) using the chain ...
144 views

### Store's unseen items sales forecasting

I am working on sales forecasting problem.I am able to provide data about which items got sold and not sold to the algorithm.How to provide algorithm information about items that are not present in ...
• 549
198 views

### Epoch greedy algorithm for contextual bandits

I'm reading the following paper on the epoch greedy algorithm for the contextual bandits problem. I have two questions http://hunch.net/~jl/projects/interactive/sidebandits/bandit.pdf I'm unsure ...
• 171
24 views

### Do I need to use Bayes to combine a sample's class probability with the performance of the overall model?

For a classification algorithm that gives the predicted probabilities for each class (ie random forest in sklearn), by default the classes are separated with a score of 0.5. After evaluating the ...
• 320
211 views

### Modeling the influence of events order on probability

The case is to model if the sequence of events influences the probability of binary target variable. We have for example five different events which occur in time (event: A,B,C,D,E). They can occur in ...
• 31
558 views

### Implement gaussian mixture model with stochastic variational inference

I am trying to implement Gaussian Mixture model with stochastic variational inference, following this paper. This is the pgm of Gaussian Mixture. According to the paper, the full algorithm of ...
41 views

### Is there a counting sketch optimized for intersections?

Popular counting sketches(loglog, hyperloglog, etc) feature natural union operations. Are there any known counting sketches that feature natural intersection operations?
• 121
2k views

### predicting probability distribution for time series

I have time series of several variables. Just in one specific case one variable is linear combination of the rest. I want to predict probability distribution (that is not only best estimate but ...
• 121
153 views

### Predicting missing data. Looking for good data predicting technique

I am analysing data for Countries Trade GDP. Some of the countries have missing GDP value for given a year. However, I have Grand Total for the entire region for that year. Is there a good data ...
1 vote
52 views

• 164
1 vote
121 views

### How to compare Poisson Point Process, ARIMA and LSTM?

I am trying to compare three forecasting techniques: A stationary stochastic Poisson-GEV: where the rate of occurrence of the events is given by a Poisson process and, it's intensity is given by a ...
1 vote
66 views

### How to derive Evidence Lower Bound in the paper "Zero-Shot Text-to-Image Generation"?

Can someone share the derivation of Evidence Lower Bound in this paper ? Zero-Shot Text-to-Image Generation The overall procedure can be viewed as maximizing the evidence lower bound (ELB) (Kingma &...
• 21
1 vote
464 views

### Use distribution probability as a feature in ML model

I built an LSMT model to predict sick cows. I also have risk factors like cow size and height (static risk factor) that I want to combine into the ML model. I found that size is geometrically ...
• 11
1 vote
241 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 ...
1 vote
46 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 ...
1 vote
24 views

### Ensemble of different reservoirs (echo state networks)

Suppose I want to do reservoir computing to classify the input to the proper category (e.g. recognizing a handwritten letter). Ideally, after training a single reservoir and testing it, there would be ...
• 111
1 vote
25 views

### different scored probability distributions for a classification model using python vs Azure ML Studio

I have a classification model which I initially built in Azure ML studio and then created a similar model in python (its similar because the algorithms in python VS Azure are a bit different so they ...
• 71
1 vote