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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How to get probability of an outcome from skewed t distribution in R

I am trying to calculate the probability of stock return to be greater than X in next 28 days, using the skewed t-distribution as it fits the best to the ...
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Generating a Sample T-Distribution in Python

I'm new to DataScience. I'm going through the stats module and the ways to create a sample T-distribution. Using the below line to generate the sample. However, my question is how is the array ...
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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 ...
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Probabilistic line of best fit

I have data about pedestrian counts from multiple sensors along a street. I know the location of each sensor. Assume that the street has total length L. I want a function F(l,t) that gives pedestrian ...
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Can an OCR model consistently recognize every digit of a long number correctly?

I'm working on OCR on scanned documents and we need to recognize the exact sequence of some printed numbers on it. Imagine you're reading a bank cheque serial number (16 digits) so the system needs to ...
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Getting actions probabilities instead of an unique prediction in Stable Baselines 3 SAC?

I try to understand how getting an actions probability table instead of an unique prediction in stable baselines 3 SAC in order to override 'predict' method to filter invalid actions. I guess the good ...
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Inverse Probability Fallacy of Maximum Likelihood Estimation

How can we justify that the parameters estimated via Maximum Likelihood Estimation are the optimal parameters for the data, given that MLE involves computing the data's likelihood as a function of the ...
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Is using Probability Classification to predict whether a restaurant will purchase the best approach?

I have a data set that contains data about restaurants in the United states including menu, foot traffic, type of cuisine, type of restaurant, and other restaurant attributes. I also have a small ...
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If a set of random vectors are independent then would the join event of the random vector from the set and another random variable independent?

If all x_i from i=1 to n are independent. And y_i is dependent on x_i. Then can we always say that all (x_i, y_i) tuples are always independent of each other? x_i is a random vector of shape mx1, y_i ...
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$p(f|X,Y) \propto p(Y|X,f)p(f)$

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 $p(f|X,Y) \propto p(Y|X,f)p(f)$...
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Negative Log Likelihood of a Gaussian Model can be negative?

I am confused about the negative log likelihood of a guassian model: Can the negative likelihood of a gaussian model be negative ? lets suppose that the variance is going to 0 faster than the MSE ...
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Probability Distributions

I was going thru a course on probability and probability distributions for data science and in that they were describing the various probability distributions with mathematical formulae. I was ...
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Reinforcement Learning: Formally, does an exponentially decaying epsilon satisfy GLIE?

I am aware that exponentially decaying exploration constants (epsilon) are used practically. Formally, though, do they satisfy the GLIE condition? Specifically, relating this to the Borel-Cantelli ...
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What does maximize average log probability mean?

In the word2vec paper (https://arxiv.org/pdf/1310.4546.pdf) that introduces the skip-gram algorithm we encounter this phrase: which says that we maximize the average log probability. Can someone help ...
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Conditional density estimation for sequences using conditional random fields

I am looking to estimate the conditional distribution of the next observation $x_{t+1} \in \mathbb{R}_+$ of a discrete-time process, given the current observation and $l$ previous observations. I am ...
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Check the validity of the distribution in the proof of No-Free-Lunch Theorem

I'm reading the proof of No-Free-Lunch Theorem (quoted at the end of this question) in Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, p.37, the author wrote: ...
Tran Khanh's user avatar
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Is probabilities mean of predicted class (RandomForest) a consistent estimator of class recall?

I'm working on a classification problem in order to predict among 50 different classes. I'm using a Random Forest classifier and I'm using the predict_proba method ...
Jerome X.'s user avatar
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Hi, I need help with this question. This quesion is based on Markov Chains. I want to answer it without using any simulation

Alinah is spending the summer at her grandparents’ farm in a small town in Iowa. The town is known for frequent changes in its weather. Each day starts off as either sunny or rainy. There’s a 50% ...
neon's user avatar
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Confusion over taking gradients in Variational Autoencoder (VAE)

I am confused as to when to hold certain parameters constant in a VAE. I will explain with a concrete example. We can write $\operatorname{ELBO}(\phi, \theta) = \mathbb{E}_{q_{\phi}(z)}\left[\log \...
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How to prove equation (7)(8) in paper A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning?

I couldn't derive the formula (7)(8) in paper https://arxiv.org/pdf/2110.01515.pdf They didn't seem obvious to me.
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Edit friendly DDPM noise space

I was reading this paper, "An Edit Friendly DDPM Noise Space: Inversion and Manipulations". In page no. 4, they have mentioned that in DDPM, noise maps of consecutive steps are highly ...
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How to visualize a changing distribution overtime, given non-uniformly sampled data with timestamps

Assume that there is a bernoulli distribution whose probability is unknown to us and may occasionally change. What we have on hand are non-uniformly sample results (0/1) with timestamp information. ...
ZeroRin's user avatar
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Bayesian Neural Network Inference

In a paper about Bayesian Neural Network, I saw the following algorithm: Algorithm 1 Inference procedure for a BNN. Define $p(\theta\mid D)$; for $i = 0$ to $N$ do Draw $θ_i \sim p(\theta\mid D)$; $...
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Intuition behind Jensen Shannon divergence between deep features of two images?

Jensen Shanon divergence is mainly used to determine the divergence between two probability distributions. Can it be used to calculate the difference between the deep feature vectors of two images? I ...
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Looking for suggestions on how to distinguish between two different distributions in my data when no labels are available

My data consists of medical claims from thousands of providers across the country. The data contains how much they were paid for each service they performed and how many units of each service they ...
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Understanding the calculations of bayesian classification rule given features with known distribution

I have some difficulties using the Bayes rule and the calculations used for estimation of a given class in a classification task if we know the distribution (and its parameters) of the feature vector: ...
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Do models of social systems suffer from prediction drift?

Background I've created a binary classification model that predicts the probability of fraud for a given sample. The choice of threshold allows me to set how many frauds are captured in the training ...
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Trying to extrapolate info from a partial data set - statistical inference

I am wondering if my logic is OK here or not. 98% of a group without a device has an event occur 2% of group with device has an event occur Since we know that correlation isn't causation I can't say ...
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probability distribution

Just wanted to know if the value we get by passing, say, random.normal(shape=(3,2)) in the Tensorflow, etc, are normally distributed or if they are randomly chosen ...
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Precision vs probability

Say I have a model which predicts a class $C_i$ from an input $X$, with a probability of 0.95 i.e $P(C_i| X)=0.95$. That would mean that if we do this over and over, then 95/100 times we would be ...
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Predicting probability of reaching a milestone -- How much data should I use from production universe to train/test model?

If I am predicting probability of a business to reach (x) milestone (classification 1), but the only data I have is live production data, how much of the production data should I use to train the ...
mithrandir's user avatar
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Get dependant probabilities in multiclassification

After training my CatBoostClassifier model I call get_proba function which returns me list of probabilities. The problem starts from an another point... I transfer that data into dataframe then to ...
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How to demonstrate two variables are orthogonal with respect to the output in a 3-D Python dataset?

I have a Python dataset with 300 samples and 3 columns: 2 independent integer variables X,Y and the dependent continuous variable ...
Adrián Pérez Diéguez's user avatar
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Calculationg perplexity (in natural language processing) manually

I am trying to understand Perplexity within Natural Language Processing as a metric more fully. And I am doing so by creating manual examples to understand all the component parts. Is the following ...
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whats the difference between these two value function definisions?

I've seen in literature two different yet similar approaches when writing the value function in an MDP: $V_\pi(s)=\sum\limits_{a\in A}\pi(a|s)\sum\limits_{s'\in S}\sum\limits_{r\in R} Pr[s',r|s,a][r+\...
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Good models for predicting whether a customer would make a purchase given details like age, gender, ethnicity, salary, etc?

I have around 30,000 data points and for those data points I have some numerical fields like customer_age, ...
Tom's user avatar
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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 ...
sander's user avatar
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How is Probability Used in Data Science? [closed]

This is my first Question so apologies if I do not stick to the standards. What I want to understand is how is all of the following topics: Probability Different Probability Distributions. Baye's ...
Yash Agarwal's user avatar
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Mathematically rigorous NLP

I'm looking for resources (books/articles/whatever) that provide mathematical formalization of NLP and statistical language theory. By that I mean clear exposition of the subject in terms of ...
TessaIncognita's user avatar
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Strategy to choose maximum value from an unknown array of n numbers

Suppose you have an array of n normally distributed numbers whose values are initially unknown(and the probability parameters are unknown too). You must choose one number and you want it to have ...
AutisticRat's user avatar
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Compute the pdf from pandas kde

I have data (features/targets in machine learning terminology), e.g. X1(t), X2(t), ... XN(t) and dependent variable y(t). I can use pandas to plot the kde's of the independent variables (X1(t),...). I ...
Socorro's user avatar
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How can I obtain the mean of a Poisson distribution given the first improbable point of the distribution?

I generated a Poisson distribution with mean equal to 3 and 10000 samples by using np.random.poisson(3,10000). The plot is the following: from this plot I see that ...
HelpNeederStudent's user avatar
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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 ...
bdwilson24's user avatar
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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 ...
hypothe's user avatar
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How does factoring probability distributions help?

I'm trying to make sense of machine learning and am reading Deep Learning by Goodfellow, Bengio and Courville. In section 3.14 they show an example of factoring a distribution then say "These ...
Alan's user avatar
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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-...
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approximating probability mass function from a large data

I am learning elementary probability; especially I am interested in learning how to find probability mass functions and density functions from data. I think I perfectly understand the theory: For ...
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Inject external prior distribution to my dataset

Input: External Information - distribution between the feature_i & binary_target Internal Dataset - tabular data. ...
orbgr's user avatar
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Correct way of calculating probability

I have some data which shows how many orders were made by a certain customer group that bought a certain product type: And the same format but showing how many refunds were made: I am trying to ...
Jonas Palačionis's user avatar
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XGBClassifier's predictions are not probabilities with objective='binary:logistic'

I am using a XGBoost's XGBClassifier, a binary 0-1 target, and I am trying to define a custom metric function. It supposedly receives an array of predictions and a DMatrix with the training set ...
João Bravo's user avatar

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