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| bio | website | |
|---|---|---|
| location | CA | |
| age | ||
| visits | member for | 2 years, 6 months |
| seen | May 18 at 0:54 | |
| stats | profile views | 77 |
I'm a phd student in computer science.
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May 26 |
comment |
Problem of “clustering” into most similar groups Could you get to the same result by first clustering with k-means, then constructing your 'groups' by taking M points from each cluster? |
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May 25 |
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Classification-results datasets what exactly is your question? How to get the classification results? |
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May 25 |
revised |
How to calculate threshold level for mutual information scores? grammar |
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May 24 |
answered | How to calculate threshold level for mutual information scores? |
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May 23 |
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Observation (evidence) in dynamic Bayesian networks observed nodes in a BN are nodes for which we have seen the actual values (e.g. from sensor data). The value has a likelihood given the node parameters. For instance, if the observed value from a sensor is assumed to have Gaussian noise about the true value and our current estimate of the true value is $x_t$, then the observed sensor value: $z_t$ has a likelihood of: $\mathcal{N}(z_t; x_t, \sigma)$. |
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May 23 |
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Observation (evidence) in dynamic Bayesian networks This question is rather unclear. Are you asking how the observed nodes in a Bayesian network affect the probability of the unobserved nodes? How to model the values for your specific problem (i.e. what distributions to use)? Clarifying your question will greatly increase our ability to provide a relevant answer. |
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May 22 |
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How to cluster LDA/LSI topics generated by gensim? You can treat the topic mixture vector for each document as its position in this latent topic space. Simply run your clustering using this as the input data. What is the point of the hard clustering though? The topic mixtures already give you a lot of information about how documents are similar/different. |
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May 22 |
revised |
What is the difference between “priors” and “likelihood”? minor notation change |
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May 22 |
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What is the difference between “priors” and “likelihood”? @NeilG, yes, you're absolutely correct - I've edited my answer to reflect this. Its sometimes written $\mathcal{L}(\theta | D)$ because the likelihood can be viewed as a function of the parameters holding the data constant. |
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May 19 |
answered | What is the difference between “priors” and “likelihood”? |
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May 17 |
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Constructing a Bayesian network from the begining You can use maximum likelihood estimates (this results in simply counting # of transactions that include fraud = yes, normalized by the total number of samples to estimate p(fraud = yes/no)). |
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May 16 |
answered | Constructing a Bayesian network from the begining |
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May 2 |
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matlab princomp latent can you post the exact code you used to run PCA? |
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Apr 23 |
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MCMC algorithm to estimate beta and variance @Matt: are you talking about understanding how to go about setting up and computing Bayesian inference for a linear regression model? If so, please edit your question to say that, if not, please explain what you mean. |
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Apr 10 |
awarded | Enthusiast |
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Apr 7 |
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Choosing attributes for clustering/classification Can you provide any context about the type of data this is and why you only have 2 instances for each class (I'm just curious)? |
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Apr 6 |
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Dealing with lots of ties in kNN model How do you get ties if $k$ is odd? What do you mean by "only using variables with >2 levels"? |
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Apr 2 |
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Is Bayesian nonlinear regression using conjugate priors possible? Depends on the sampling distribution (likelihood). A table showing the conjugate prior of various likelihood models is shown on wikipedia: en.wikipedia.org/wiki/Conjugate_prior |
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Apr 2 |
answered | Simple introduction to MCMC with Dirichlet process prior? |
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Apr 1 |
awarded | Popular Question |
