# 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.

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### 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 ...
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### How to approach this: Percentage change in one KPI leading to change in other KPIs?

I want to know how can I approach or model this problem. I have 7 KPIs (3 of them dependent on each other) and one main KPI (total 8 KPIs). I want to understand effect of these 7 KPIs on the main KPIs....
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### Approach for analyzing marketing campaign

Consider my users has to do "Action A" at their own pace to be able to continue using our service. Then, we run a campaign to push the user to do "Action A" at a quicker pace by ...
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### Predicting probabilities in Neural Networks

I have 1000 number of inputs in a sample each ranging between 0-1 as shown: ...
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### Probabilistic Machine Learning model to match spatial data

I have spatial data from multiple sources. This data consists of ID, lat, long, and time. My goal is that given a new lat-long, the model needs to return (preferably with a probability) the data ...
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### 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 ...
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### Which is better KL- Divergence or Bhattacharya(Hellinger) Distance

I'm beginner in probability and statistics. I came across the concept of comparing two probability distributions. KL-Divergence and Bhattacharya(Hellinger) Distance are used to compare two probability ...
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### How to choose products based on Number of good, bad and total reviews?

Let us suppose, I have few scenarios for products with good and bad reviews. P1: 1000 Good, 1 bad P2: 100 good, 10 bad P3: 20 Good, 0 bad P4: 10000 good, 500 bad ...
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### 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 &...
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### Calculate probability in a dataset

What is a good way to calculate probabilities in a dataset of samples? Each sample is a measurement, that is usually 1 or 0. The goal is to calculate probabilities based on all feature rows. Simple ...
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### how to compute bernoulli entropy?

I am reading gail implementation code in openai baselines. they compute bernoulli entropy as one of the loss in adversary network loss function. In their code, they implement bernoulli entropy as ...
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### 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 ...
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### Training set Distribution and Activation function/Loss function correlation

How should the probability distribution of the training set influence the choice of the activation function / loss function? For instance if I have a Multinoulli distribution, which activation ...
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### Independence of Features assumption in Naive Bayes

How do we know if your features in my dataset are independent before applying Naive Bayes? Basically I want to know is it possible for us to get an idea before training our model if Naive Bayes will ...
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### I need help to write an essay about: probabilistic method || Occam's razor || mathematics in the 21st

I am interested in one of the master's programs in Data Science. In the application process I need to submit an essay of 1,000 words about one of the following topics: Drawbacks of the probabilistic ...
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### Cosine Similarity: Works with TF-IDF Vectors OR with Probability Vectors?

Using Cosine Similarity is a common method to calculate Semantic Textual Similarity. And it is particularly useful when comparing Sentence Embeddings provided by the Universal Sentence Encoder. ...
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### Qunatify total time saved by prioritizing tasks based on the failure rate probability of each task

I am trying to solve a problem where I am trying to prioritize the tasks in a job based on the failure rates of each task. For ex: ...