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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1answer
63 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 ...
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1answer
27 views

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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15 views

Identify causal feature in a classification model

Assume I have a model $f(x;b_1,b_2,b_3,b_4)$ which maps a 4-dimensional vector into a binary classifier e.g logistic regression with 4 parameters to create churn-classifier. Say, for instance, that $...
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6 views

Understanding forward process in diffusion models

I was reading a blog on diffusion models where I came across this expression. I didn't understand why it is \begin{align} \sqrt[]{1-\beta \small{t}}*\large{x}\small{t-1} \end{align} and what exactly ...
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2answers
97 views

How to predict when an appointment will be scheduled?

I have a dataset of tens of thousands of appointments. Appointments have a created date and scheduled date. Something like this: ...
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1answer
200 views

Very low probability in naive Bayes classifier 1

I have some training data (TRAIN) and some test data (TEST). Each row of each table contains an observed class (X) and some columns of binary (Y). I'm using a Python script that is intended to predict ...
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12 views

Though process to calculate error rate for a classification algorithm with 1000 objects?

I am trying to solve this question A classification algorithm classifies 1000 objects in to one of two classes. It incorrectly classifies 13 out of 100 class 1 objects and 53 class 2 objects. (a) What ...
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15 views

How to perform a Monte Carlo simulation with continuous sampling using discrete quantiles?

Assume I have registered the duration of 10 tasks and built the table below with using this data: Duration For how many tasks it happened 4 days 5 task 6 days 2 task 8 days 2 task 10 days 1 task ...
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1answer
114 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 ...
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3answers
2k 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 ...
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1answer
39 views

How to understand Bionomial Theroem and the Recursion Rule?

In this video from EDX, the instructor explains the binomial theorem as: Binomial Theorem: When you calculate $(a + b)^n = a^n + C(1)a^{(n-1)b} + C(2)a^{(n-2)b^2} + ... + C(n-1)ab^{(n-1) + b^n}$ The ...
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How to get probabilities values with keras?

tensorflow version = '1.12.0' keras version = '2.1.6-tf' I'm using keras with tensorflow backend. I want to get the probabilities values of the prediction. I want the probabilities to sum up to 1. ...
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2answers
164 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 ...
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1answer
49 views

Compare cross validation values of Bernoulli NB and Multinomial NB

I'm testing the Multinomial NB and Bernoulli NB on my dataset and I'm using the cross validation score to better understand which of the two algorithms work better. This is the first classifier: ...
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1answer
19 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 ...
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2answers
34 views

Weighted Probabilities

With numpy, how would I select an item with weighted probability? ...
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0answers
10 views

Tensorflow Probability Implementation of Automatic Differentiation Variational Inference with Mixtures

In this paper, the authors suggest using the following loss instead of the traditional ELBO in order to train what basically is a Variational Autoencoder with a Gaussian Mixture Model instead of a ...
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1answer
49 views

derivation for expected value for variance

Hi Im taking a course about probability distribution in datascience and below is derivation of the expected value for the variance Variance = expected value of the squared difference from mean for ...
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1answer
31 views

What probability distribution would be more appropriate for monthly rate of going to the store?

Part of a model I am making includes the frequency with which people go shopping for a given good (e.g. people on average go to the supermarket some n times a month). I am trying to figure out what ...
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11 views

Multi-modal histogram and real-world measurements

I have a histogram of real-world measurements of the wind speed at a given site. There are many 0's in the dataset, presumably because the wind was far to gentle to trigger the sensor into reading ...
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2answers
96 views

Intuition behind using non-hypercubic kernels in density estimation

Suppose that we perform density estimation in m-dimensional space: we estimate the value $p(a)$ for some point $a$ given observations $\{x_1, \dots, x_n \}$. It is known that if region $A \subset \...
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8 views

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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0answers
41 views

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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1answer
77 views

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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2answers
47 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 ...
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0answers
23 views

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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19 views

Deep Learning book - trying to understand Bernoulli formulas

In the section 6.2.2.2 Sigmoid Units for Bernoulli Output Distributions of The Deep Learning Book there is a section: (z is defined as $z=w^Th+b$ and $\hat{y}=\...
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1answer
32 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 ...
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2answers
54 views

How to generate a random sample and distribute values based in an probability distribution?

I want to generate a random sample based on this probability distribution: The line is the KDE of the histogram. My random sample will have n values, the value is ...
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1answer
40 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 ...
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1answer
583 views

What does a predicted probability really mean, without considering the accuracy of the underlying model?

Say I've built a (completely unrealistic) classification model in Keras that gives me 1.00 accuracy. And next, I would like to use my model on some new, unseen data, and use ...
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2answers
4k views

Calculating an estimate of KL Divergence using the samples drawn from distributions

Given two sets of samples drawn from two different distributions, is it computationally possible to get an estimate of KL-Divergence between the two distribution using these samples? Here I am ...
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1answer
146 views

correcting conditional and marginal distribution in transfer learning

I understand that in case of transfer learning, we can have the target and the source data having different domain distributions. In such cases, authors in many papers suggest bringing the marginal ...
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1answer
27 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 ...
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1answer
90 views

Probability of Gaussian Naive Bayes

How would I go about attaching a probability to the prediction outputted by a Gaussian Naive Bayes model ? I'm asking because the predict_proba function U can use with sklearn's Gaussian Naive Bayes ...
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3answers
2k views

Non-mutually exclusive classification sum of probabilities

So I have the following problem: I realized (while writing my master thesis) that I am still not sure/have vague descriptions of some of the machine learning principles. I already asked one question ...
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1answer
110 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 ...
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2answers
77 views

Notation for features (general notation for continuous and discrete random variables)

I'm looking for the right notation for features from different types. Let us say that my samples as $m$ features that can be modeled with $X_1,...,X_m$. The features Don't share the same distribution (...
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2answers
31 views

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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0answers
39 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 &...
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2answers
38 views

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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1answer
234 views

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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2answers
98 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 ...
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1answer
34 views

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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2answers
47 views

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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0answers
38 views

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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28 views

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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9 views

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: ...
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1answer
58 views

Extending DTW 1-NN Classification to On-line Scenario

I am familiar with Dynamic Time Warping classification using a 1-nearest neighbour approach. However, in most benchmark datasets and applications, it used ex-post, i.e. classifying a time series after ...
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0answers
45 views

Using softmax for multilabel classification (as per Facebook paper)

I came across this paper by some Facebook researchers where they found that using a softmax and CE loss function during training led to improved results over sigmoid + BCE. They do this by changing ...

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