Questions tagged [generative-models]

A probabilistic or statistical model thought about as describing how the values in a sample is actually generated, and not only as a description or approximation.

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Using generative models for classification

I think I never saw a generative model used for a classification task: usually a discriminative model is used; Sometimes (AFAIK, with deep neural networks) a generative model is created as a pre-...
110 views

Beginner working with generative models

I'm getting a set of 20-dimensional feature vectors. I'm getting a 100 vectors per second. These feature vectors are occuring in a sequence, where one feature vector depends on the one that came ...
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Linear discriminant analysis likelihood function

It's a lot of time that I don't do probability stuff, so probably this is a very naive question for most of you. Anyway.. For the linear (fisher) discriminant we have an inpu $X$ and an output $Y$ ...
5k views

Modern Use Cases of Restricted Boltzmann Machines (RBM's)?

Background: A lot of the modern research in the past ~4 years (post alexnet) seems to have moved away from using generative pretraining for neural networks to achieve state of the art classification ...
288 views

Are all generative Models based on Bayes?

Reading about deep learning I encounter various different kinds of hierarchical networks, many of which are generative. 1) Are all of the generative networks based on Bayes? 2) If not, how do they ...
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Question regarding Gaussian Discriminant Analysis, and Generative Learning models

In lecture today, my professor mentioned in the context of GDA and Generative learning, we would like to learn the joint probability $P(x, y)$, where $x \in \mathbb{R}^n$ and $y \in \{+1, -1\}$. ...
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How does the reparameterization trick for VAEs work and why is it important?

How does the reparameterization trick for variational autoencoders (VAE) work? Is there an intuitive and easy explanation without simplifying the underlying math? And why do we need the 'trick'?
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Prediction without labelled data

I am working on a churn prediction model, where I am trying to predict probability of employee churn. For each employee I have the following features 1) Role 2) Total experience 3) Current experience ...
627 views

What is the relationship between generative models and density estimation?

If aren't they synonymous, what distinguishes the one from the other? Is probability density estimation a certain kind of generative model? Can any generative model be regarded as density estimation?
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Difference between probabilistic generative, discriminative models and "discriminant functions"?

I have been thoroughly confused by a bunch of online resources regarding this issue. I would really like a simple explanation about the differences between generative and discriminative approaches, ...
269 views

Is there a more precise definition of generative models?

Intuitively, a generative model is one that we can generate good data from. What I'm looking for is a formal definition of "good data." For example, for any classification model I could generate ...
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Treating multiple Dichotomies combined?

Let's say I am interested in choosing a new country $c_1, \ldots, c_k$ to live in. For some reason I can only apply to one country and only once. I know for each country a set of 2000 observations (...
788 views

Topic Words Selection in Topic Modeling

I understand how generative model of topic modeling works; for each topic there is a distribution of words, and for each document there is a distribution of topics. Question is how words are ...
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Difference and connection between generative learning, discriminative learning and max-margin learning

I once heard that, generative learning, discriminative learning and max-margin learning can be separated in terms of their respective definition of loss function. I am not sure how to achieve that?
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Generative modeling of a mix of continous and discrete variables

I'm trying to build a generative model to run a Monte Carlo simulation. The existing data consist of a combination of discrete and continuous variables. Suppose I have a number of people... ...
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Statistical model of a website

I know that HMMs can be used to construct statistical models of text. Thus, we can generate text according to this model, and compute the likelihood of a text sample under the model. What tools are ...
224 views

Beyond Fisher kernels

For a while, it seemed like Fisher Kernels might become popular, as they seemed to be a way to construct kernels from probabilistic models. However, I've rarely seen them used in practice, and I have ...
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Generative model that penalizes clumping of data

I'm interested in modeling a generative process that encourages data to be "evenly distributed" over its support, i.e. clumping of data points is penalized. For example, if I have a mixture ...
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Why are Gaussian "discriminant" analysis models called so?

Gaussian discriminant analysis models learn $P(x|y)$ and then apply Bayes rule to evaluate $$P(y|x) = \frac{P(x|y)P_{prior}(y)}{\Sigma_{g \in Y} P(x|g) P_{prior}(g) }.$$ Hence, they are generative ...
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Generative vs. discriminative

I know that generative means "based on $P(x,y)$" and discriminative means "based on $P(y|x)$," but I'm confused on several points: Wikipedia (+ many other hits on the web) classify things like SVMs ...