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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What is the meaning of “probability distribution of p(x)” of something uncountable?

I'm studying VAE and new to both of the neural network and the statistic. After some researches, I could understand the rough concept of VAE. But what makes me confused is, the meaning of probability ...
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34 views

Probabilities of a Poisson distribution not making sense

I am trying to find probabilities of orders a restaurant gets on Sunday's. For last 6 months average orders are 1000 without any big anomalies like 700 or 1300. This is a case of poisson distribution &...
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Should scaling be done for mixed data (categorical and numerical)?

My dataset contains 13 attributes consisting of 10 Numerical and 3 Categorical attributes and Target. It has 180 observations ...
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how to use Bayesian theorem and probabilistic analysis? [closed]

could someone help me to solve this problem please ? Your prize Rapid Ripe tomato plant has flowered and is ready to start producing fruit. If all goes well, ...
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71 views

What do we learn from training a dataset for logistic regression

What do we learn from training our dataset in Logistic Resgression? Like in Linear Regression, with the help of training set we are able to generate a best fit line(y = mx+c) where m and c come from ...
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How to find the probabilities of certain events occurring in a defined sequence?

Good day, everyone! I have a problem where I have to program something like this: I have some arbitrary number of events, let's call then Event A, B, C, D, E, F, ... and so on. Now they occur in ...
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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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795 views

Solving Bayes Theorem equation -> I can't calculate proper result

I am solving questions for an edx course on Machine Learning. One particular question is giving me a problem: Assume a patient comes into the doctor’s office to test whether they have a particular ...
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What is meaning of different symbols in image?

I am learning about Deep Generative Models, tutorials all over the places use symbols and no one actually explaining the meaning of it. Can you please suggest the meaning of these terms?
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Pytorch doing a cross entropy loss when the predictions already have probabilities

So, normally categorical cross-entropy could be applied using a cross-entropy loss function in PyTorch or by combing a logsoftmax with the negative log likelyhood function such as follows: ...
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Classification targets with heterogenous meanings

I am training a classification model on a dataset of users on a website and each has 100 different measurements of their behaviour on the platform. Most of these ...
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1answer
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Sample Space of Longest Run

I am trying to find a way to calculate all possible combinations of a sequence that have a certain length of long run. When answering questions regarding sequences of heads and tails, sometimes ...
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Data-generating probability distribution, probability distribution of a dataset, in ML

In Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press; 2016 Nov 10. http://thuvien.thanglong.edu.vn:8081/dspace/bitstream/DHTL_123456789/4227/1/10.4-1.pdf p. 102 (for example), it is said ...
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F-test for comparing the mean of two groups

What would be the procedure, in terms of F-test, if I would like to check if the mean of one group is greater than the other group (alternative hypothesis: $\mu_1 > \mu_2$)? And also what would ...
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Seeking advice on knowledge discovery

Background Information I work for a fire department in Florida and the fire chief posed a question to me; At any given moment in time during the calendar year 2018, how many fire trucks are busy, ...
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How do I properly write scipy.stats.binom.cdf() details

I need to calculate the probability of my random variable being $\le 0$. It's a binomial distribution, $10000$ trials, probability of success is $\frac{10}{19}$ (roughly $0.53$). How do I properly ...
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1answer
45 views

How can I make ROC and compute AUC?

I created a boosting tree and got the probability for each tuple in my testing set. But I'm confused on how to combine each probability. Can someone tell me how to combine the probabilities?
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How is the standard deviation of VAE's obtained?

I am trying to build a Variational Autoencoder. I was looking at various codes online and found most of them in some way or another copy Francois Chollet (Google researchers) code. Now my main ...
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1answer
182 views

Predict how many days late or early someone will finish their work

So I have a set of deadlines and people, with a database of when those people finished their previous work and how much after the deadline it was, as well as when the work was given. The work itself ...
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1answer
166 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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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 ...
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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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554 views

Why does a belief network need to be represented using a directed acyclic graph (DAG)?

I would have thought that it's because DAGs preserve the dependency relationships between the variables, but I am currently unsure. Thanks
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986 views

Why do people use CrossEntropyLoss and not just a softmax probability as the loss?

I don't understand why one would add additional complexity to log, probabilities for the loss function of a classification Neural Network. What benefit does that have, as opposed to just using the 0-1....
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Why prior in MAP could be ignored?

A posterior $p(\theta\vert x) = \frac{p(x \vert \theta)p(\theta)}{p(x)} $ Many materials say that since the $p(x)$ is a constant, the $p(x)$ can be ignored. Thus, $p(\theta\vert x) \propto p(x \vert ...
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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: ...
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38 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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How to elegantly caclulate probability distribution parameters for a particular random variable given some observed data?

I am using bnlearn package and R to learn Bayesian Network structure and also fit it using Maximum Likelihood estimation(MLE). bn.fit uses MLE to learn[as i understand] a generalized probability ...
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1answer
70 views

Using classifier's probabilities as independent variables to predict Y

Suppose you have a classification task y~X with (n_samples,m_features). A colleague told me that it is correct to run r different classifiers to predict y based on X and then use the probabilities ...
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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 ...
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1answer
268 views

Layman's description of PDF and CDF [closed]

Can anyone please explain what a PDF and CDF are in simple words. (Please don't define it from wiki.)
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Probability Distribution of the Process Duration Time based on the Process Status Measurements

I have data of execution of several processes. Each execution is identified by the process_id. The duration of the execution is measured by external agents, testing ...
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2answers
364 views

xgboost or lightgbm to handle Binomial problems [duplicate]

I have a dataset containing a column of trials, a column of successes and other features; and, obviously, I can generate a probability column. I would like to use gradient boosting methods (like ...
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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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31 views

the probability distribution of dependent variables

There are three variables, X3 is a function of X1 and X2, ...
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2k views

Loss Function for Probability Regression

I'm 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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Trying to find the correlation between inputs and output

I have tried the pandas code for trying to find out the correlation between the output and the inputs I am feeding. Here is the code: ...
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2answers
144 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 ...
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116 views

Historical weather data with machine learning? [closed]

My company gave me a task to build some weather forecasting. I have now historical weather data for 10 years (temperature, precipitation in mm, humidity and etc. more than 30 features total). We need ...
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2answers
684 views

Do I need to correct predict_proba by training fraction?

Many algorithms provide a predict_proba function indicating probability of a case to belong to that class (e.g. https://scikit-learn.org/stable/modules/generated/...
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1answer
37 views

How to predict unknown(hidden) value by incomplete value or partly recorded value

Let me make it clear by make an example: Suppose I knew a person's cost each month for 3 years like: 2016Jan : $2500 2016Feb : $4000 2016Mar : $3500 ... Just according to this, can I ...
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Comparing two sets of exponential data T(t)=−e^(−kt)

I have two sets of exponential data (temperature measurements) of the form: $T(t)=−e^{(−kt)}$. $k$ is a constant that determines the rate of temperature change. 1 temperature measurement was taken ...
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1answer
55 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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1answer
43 views

objective in policy gradient equation?

I don't understand how this was deduced from first equation to second expectation. Is it from conditional probability theory? I checked but still can't understand. From wikipedia, the expectation of a ...
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2answers
147 views

Maximum likelihood estimation vs calculating distribution parameters “manually”

I'm sorry for asking probably elementary question, but I cannot understand how estimating probability distribution parameters using maximum likelihood estimation method differs from calculating these ...
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1answer
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Statistical machine translation word alignment for FR-ENG and ENG-FR: what is p(e) and p(f)?

I'm currently trying to implement this paper, but am struggling to understand some of the math here. I'm pretty sure I understand how to implement the E-step, but for the M-step, I'm confused on how ...
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1answer
84 views

Capturing movement importance - logistic regression output

I'm studying some event for a set of objects that can be plotted on a square $[0, 100] ^ 2$. I have used logistic regression to calculate probabilities that event occur for different objects and the ...
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1answer
248 views

Can someone please explain what this sample function is upto?

So there is a function in Dino_Name_Generator at Deeplearning.ai notebook ...
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2k views

Why does the naive bayes algorithm make the naive assumption that features are independent to each other?

Naive Bayes is called naive because it makes the naive assumption that features have zero correlation with each other. They are independent of each other. Why does ...
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mathematical accurate definition of the binary independence model

I have a hard time understanding the exact mathematical meaning behind the binary independence model. On wikipedia we can see the following definition or similarly in the book from Manning and ...