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Questions tagged [probability-distribution]

For questions related to AI theory that relies on the knowledge of a distribution of probabilities across one or more dimensions affecting probability. Such a distribution may be in discrete buckets, such as quartile, octile, or percentile conventions or continuous functions based on some closed form (algebraic formula). Distributions of probability are key in planning, natural language handling, and other AI objectives.

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Why we multiply probabilities with support to obtain Q-values in Distributional C51 algorithm?

In 'Deep Reinforcement Learning Hands-On' book and chapter about Distributional C51 algorithm I'm reading, that to obtain Q-values from the distribution I need to calculate the weighted sum of the ...
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Normalizing flow probabilities

I am using a normalizing flow (Neural Spline Flows) to approximate a probability and after some training, the average loss is around 0.5 (so logprob = -0.5). However, when I am trying it on some new ...
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Deciding std. deviation for policy network output?

When I try to fit a Normal Distribution to the output of a policy network, for a continuous action space problem, what should be its standard deviation? mean for the distribution will directly be the ...
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What is probability distribution in machine learning?

If we were learning or working in machine learning field then we frequently come across this term probability distribution. I know what probability, conditional probability and probability ...
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How does maximum approximation of the posterior choose a distribution?

I was learning about the maximum a posteriori probability (MAP) estimation for machine learning and I found a nice short video that essentially explained it as finding a distribution and tweaking the ...
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Why is Jensen-Shannon divergence preferred over Kullback-Leibler divergence in measuring the performance of a generative network?

I have read articles on how Jensen-Shannon divergence is preferred over Kullback-Leibler in measuring how good a distribution mapping is learned in a generative network because of the fact that JS-...
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Query on another perspective on Deep Learning

At least at some level, maybe not end-to-end always, but Deep Learning always learns a function, essentially a mapping from a Domain to a Range. The Domain and Range, at least in most cases, would be ...
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Probabilistic classification - normalize results

I have a probabilistic classifier that produces a distribution over my 3 classes - C1, C2, C3. I want to compare some new points I'm classifying to each other, to see which one is the best fit for a ...
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58 views

Why is the entire area of a join probability distribution considered when it comes to calculating misclassification?

In the image given below, I do not understand a few things 1) Why is an entire area colored to signify misclassification? For the given decision boundary, only the points between $x_0$ and the ...
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What to do when PDFs are not Gaussian/Normal in Naive Bayes Classifier

While analyzing the data for a given problem set, I came across a few distributions which are not Gaussian in nature. They are not even uniform or Gamma distributions(so that I can write a function, ...
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1answer
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Why is the expectation calculated over finite number of points drawn from a probability distribution?

This is from the book Pattern Recognition by Bishop. Why is expectation here a simple average? Why is $f(x)$ not being multiplied by $p(x)$?
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Which loss functions for transforming a density function to another density function?

I am looking at a problem which can be distilled as follows: I have a phenomenon which can be modeled as a probability density function which is "messy" in that it sums to unity over its support but ...
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How are the parameters of the Bernoulli distribution learned?

In the paper Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask, they learn a mask for the network by setting up the mask parameters as $M_i = Bern(\sigma(v_i))$. Where $M$ is the ...
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Unit integral condition on the output layer

I want to train a neural network on some input data from a probability distribution (say a Gaussian). The loss function would normally be $-\sum\log(f(x_i))$, where the sum is over the whole data (or ...
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114 views

Why can we approximate the joint probability distribution using the output vector of an LSTM?

In the paper, Contextual String Embeddings for Sequence Labeling, the authors state that \begin{equation} P(x_{0:T}) = \prod_{t=0}^T P(x_t|x_{0:t-1}) \end{equation} They also state that, in the LSTM ...
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75 views

Standard deviation of the total input to a neuron

Raul Rojas' Neural Networks A Systematic Introduction, section 8.2.1 calculates the standard deviation of the output of a hidden neuron. From: $$ \sigma^2 = \sum^n_{i=0}E[w_i^2]E[x_i^2] $$ When I ...
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1answer
46 views

Binary vector expected value

Raul Rojas' Neural Networks A Systematic Introduction, section 8.2.1 calculates the variance of the output of a hidden neuron. Raul Rojas says that "for binary vectors we have $E[x_i^2] = \frac{1}{3}$...
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313 views

What loss function to use when labels are probabilities?

What loss function is most appropriate when training a model with target values that are probabilities? For example, I have a 3-output model. I want to train it with a feature vector $x=[x_1, x_2, \...
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40 views

Can the normalization factor for the belief state update be zero?

In order to update the belief state in a POMDP, the following formula is used: $$b'(s')=\frac{O(a, s', z) \sum_{s\in S} b(s)T(s, a, s')}{\mathbb{P}(z \mid b, a)}$$ where $s$ is a specific state in ...
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Can we derive the distribution of a random variable based on a dependent random variable's distribution?

In the diagram below, there are three variables: X3 is a function of (depends on) X1 and X2, ...
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What kind of distributions can be used to model discrete latent variables?

If we take the vanilla variational auto-encoder (VAE), we $p(z)$ is a Gaussian distribution with zero mean and unit variance and we approximate $p(z|x) \approx q(z|x)$ to be a Gaussian distribution as ...