# Tagged Questions

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### What is the posterior probability of the data given the model used for model averaging with Bayesian Logistic Regression?

I am trying to learn about Bayesian Model Averaging using Bayesian Logistic Regression (Genkin, A., Lewis, D. D., & Madigan, D. (2007). Large-scale Bayesian logistic regression for text ...
132 views

### Bayesian MLPs using the MCMC methods - any tricks of the trade?

Having used the NETLAB library for MATLAB to implement Bayesian Multi-Layer Perceptron (MLP) neural networks using MacKay's evidence framework, I am now experimenting with Markov Chain Monte Carlo ...
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### Reconstruct a “blocky” picture?

Consider a finite set $A$. Let the sample space be $A\times A$. We have an unknown probability distribution $f$ on this sample space. Now this probability distribution has a "blocky" property, which I ...
59 views

### Sufficient number of sample to learn Bayesian network?

I want to construct Bayesian network for a 800 genes(genes are my node/variables). I have only 30 cancer samples and 30 normal sample.so I want to create network for cancer samples and for the normal ...
81 views

### Dirichlet process mixture model with Bayesian hierarchical clustering

I am doing Bayesian hierarchical clustering. From my understanding, there are three basic points for this algorithm. Use marginal likelihoods to decide which clusters to merge Asks what the ...
142 views

### What is the relationship between graphical models and hierarchical Bayesian models?

I've searched a good bunch of literature but have failed to find an exact distinction between the two. My impression is that in the Machine Learning literature you'll find allusions to hierarchical ...
97 views

### Direct Sampling from posterior distribution

Why is direct sampling from the posterior distribution intractable?
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### Intuitive understanding of Local Probability Distribution

I'm learning Bayesian network. I have problem in intuitive understanding of Local Probability Distribution. Can anybody explain to me what it is?
55 views

### Variational Posterior Dirichlets in LDA

I am running the c code for LDA provided on David Blei's website. The code outputs several files. The output file final.gamma is supposed to include the "Variational Posterior Dirichlets". If I ...
102 views

### Good libraries for working with probabilistic graphical models?

Could someone recommend some well-maintained and up-to-date libraries for working with probabilistic graphical models? I noticed that there are some libraries for R listed here and one for C++, but ...
123 views

### regarding conditional independence and its graphical representation

When studying covariance selection, I once read the following example. With respect to the following model: Its covariance and inverse covariance matrix are given as follows, I do not understand ...
239 views

### How to compute the maximum a posteriori probability (MAP) estimate with / without a prior

I am a newbie in this area so I hope someone could explain the following problem to me in plain English. Assume I want to use MAP to estimate some parameters on the basis of some observations. I know ...
165 views

### Estimating parameters in multivariate classification resulting zero determinant sample covariance matrix

Newbie here typesetting my question, so excuse me if this don't work. I am trying to give a bayesian classifier for a multivariate classification problem where input is assumed to have multivariate ...
132 views

### Are posterior probabilities from a Naive Bayes classifier reliable?

I have read that the posterior probabilities of Naive Bayes classifiers are unreliable. Is this true? and if so, in what sense, and why? Specifically, I am interested to know if the probabilities can ...
100 views

### What kind of plot am I looking at?

I stumbled on to these following two slides (slides 21 & 22 on a machine learning tutorial found here): The first is obviously an $x,y$ scatterplot of height and weight. But what is the ...
50 views

### ML: A generalized human voting/recommendation system

I'd like to create a certain kind of voting/recommendation system. I'm sure there must be a name for what I'm trying to do, but I'm not sure. Basically, I start with a distribution of binary vectors ...
75 views

### The meaning of convergence in Variational Inference?

My friend and I are discussing about the convergence of Variational Inference, especial for Expectation Propagation method. After running some loops, the likelihood of my graphical model can be ...
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### Why doesn't ML point estimate equal MAP point estimate even though I'm using uniform prior?

I asked a previous question about why the ML and MAP estimates are the same when using a uniform prior (How does a uniform prior lead to the same estimates from maximum likelihood and mode of ...
289 views

### How does a uniform prior lead to the same estimates from maximum likelihood and mode of posterior?

I am studying different point estimate methods and read that when using MAP vs ML estimates, when we use a "uniform prior", the estimates are identical. Can somebody explain what a "uniform" prior is ...
75 views

### Trouble reading multinomial naive bayes notation

$C_{MAP}$: most likely class (i.e., "maximum a posteriori") $C_{NB}$: Naive Bayes x: document d is represented as $x_n$ ...
46 views

### How do I incorporate personalization to a Bayesian ranking engine?

I'm looking to quickly get smart on how to add personalization into a Bayesian-based recommendation system. I'm using clickstream data and Bayesian statistics to estimate probabilities of purchase ...
277 views

### Neural Network Black Box Workarounds

I am dealing with a data set that includes rich textual data (e.g., blog entries, magazine articles, essays, book reviews, etc.) as well as a host of proprietary metrics, including numerous ...
1k views

### Next steps after “Bayesian Reasoning and Machine Learning”

I'm currently going through "Bayesian Reasoning and Machine Learning" by David Barber and it is an extremely well written and engaging book for learning the fundamentals. So a question to someone who ...
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### Predicting with Relevance Vector Machines

I am trying out this Matlab toolbox for Relevance Vector Machines by Tipping: http://www.miketipping.com/sparsebayes.htm This has an implementation of Relevance Vector Machines, and generates pretty ...
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### Learning parameters of non-parametric Bayesian models

I have a sample of Chinese restaurant process which I want to model as Pitmanâ€“Yor process. How do I determine parameters of Pitman-Yor model from given sample? For Dirichlet process I would just use ...
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### How can I use Bayes rule for this question given additional data

I am required to use the Naive Bayes classifier to classify example 8, to see whether it is poisonous or not. I gained the following results: p(x|Poisonous=Y) = 0.0267857 and p(x|Poisonous=N) = ...
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### Understanding the derivation of an equation in LDA modeling

When reading the derivation of LDA models, I usually get the following equations. I do not quite understand the second step, where $p(\mathbf{z}_{-i},\mathbf{w}|\alpha,\beta)$ was removed. Is that ...
88 views

### How to identify a new pattern in a URL with a machine learning algorithm (Text mining)

I am trying to identify new patterns after analyzing a number of URLs. So let's say, I am investigating the hypothetical website Yoohle.com and their URLs have the following structure. domain = ...
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### Bayesian learning of tree distribution

this is my first post here. I'm currently trying to compute the posterior predictive likelihood for a tree-structured distribution, following the paper Tractable Bayesian learning of tree belief ...
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### Estimating probabilities using Bayes rule?

I am working on a past exam paper. I am given a data set as follows: Hair {brown, red} = {B,R}, Height {tall, short} = {T,S} and Country {UK, Italy} = {U,I} (B,T,U) (B,T,U) (B,T,I) (R,T,U) (R,T,U) ...
107 views

### Using priors to detect an effect? logistic Bayesian regression

I have designed an idea and am looking for similar approaches in other literature/areas or if I have applied the Bayesian concepts wrongly. Here is a statement of my problem: I am modeling the ...
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### what should be the parametric form of the l2 regularization in a Bayesian setting?

In a Bayesian setting for parameter estimation, what should be the parametric form of the prior distribution in order to perform l2 regularization?
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### Unscented Kalman filter-negative covariance matrix

I have recently started working on the unscented Kalman filter. I coded the numerically stable version (i.e., square root Kalman filter) and used MATLAB for implementing. In the final update step, ...
120 views

### Learn a joint distribution from incomplete samples

Suppose I want to learn a joint distribution $p(x_1, \ldots, x_n)$ and have a collection of samples $x^k_1, \ldots, x^k_n$ for each $k$. Assume some values $x^k_i$ are unknown, so the samples are ...
201 views

### Properties of conditional probability distributions

This is a problem from a machine learning pset that I'm self-learning from http://www.seas.harvard.edu/courses/cs281/assignment-1.pdf. Suppose we are provided with a hierarchy of three ...
225 views

### Can anyone tell me why we always use the Gaussian distribution in Machine learning?

For example, we always assumed that the data or signal error is a Gaussian distribution? why? I have asked this question on stackoverflow, the link: ...
101 views

### Adding training examples to Bayesian classifier reduces accuracy

I'm working on a problem to predict/classify overall sentiment of a large amount of text, which I can verify on the next day. Each data point is a day and is composed of multiple articles. I bin the ...
378 views

### Bayes decision boundary of Figure 2.5 in Elements of Statistical Learning

When I read "Elements of Statistical Learning", I met some difficulty in calculating the Bayes decision boundary of Figure 2.5. In the package ElemStatLearn, it ...
198 views

### What is the difference between Informative (IVM) and Relevance (RVM) vector machines

I'm trying to understand if there is any specific difference between Informative IVMs and Relevance RVMs other than the terminology. I've not seen anything explicit. When I'm reading about vector ...
1k views

### How to write a poker player using Bayes networks

This is my first question on stackexchange and also my first time implementing a Bayesian network so I will apologize ahead of time for any novice mistakes I make. The goal of my project is to ...
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### What is the appropriate machine learning algorithm for this problem?

I am working on a problem which looks like this: Input Variables Categorical a b c d Continuous e Output Variables Discrete(Integers) v x y Continuous z The major issue that I am ...
130 views

### Validation techniques for hierarchical model

I have a hierarchical model that I need to validate. My model is as follows: we have a collection of $\lambda_i$ that we draw from $Gamma(\alpha,\beta)$. Then, we draw our data point $y_i$ from ...
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### Are there any ways to update SVM model incrementally like Bayesian or k-NN classifiers? [duplicate]

Possible Duplicate: Can SVM do stream learning one example at a time? It takes 30 minutes to create SVM model from the whole dataset. The training time is growing as I get more new samples. ...
2k views

### The input parameters for using latent Dirichlet allocation

When using topic modeling (Latent Dirichlet Allocation), the number of topics is an input parameter that the user need to specify. Looks to me that we should also provide a collection of candidate ...
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### Two R packages for topic modeling, LDA and topicmodels?

It seems that there have two R packages for running Latent Dirichlet Allocation. One is LDA, authored by Jonathan Chang; and another is authored by Bettina GrĂ¼n and Kurt Hornik. What are the ...
142 views

### Measuring information content of a random variable in Naive Bayes classifier

I'm trying to improve accuracy in a Naive Bayes classifier that uses a bunch of features. I have a hunch that removing some features may actually improve performance. My reasoning is for a ...
335 views

### How are classifications merged in an ensemble classifier?

How does an ensemble classifier merge the predictions of its constituent classifiers? I'm having difficulty finding a clear description. In some code examples I've found, the ensemble just averages ...
1k views

### Are there any tutorials on Bayesian probability theory or graphical models by example?

I've seen references to learning Bayesian probability theory in R, and I was wondering if there is more like this, perhaps specifically in Python? Geared towards learning Bayesian probability theory, ...
Suppose you have recorded a set of paths in the $y,t$ plane, with $y = f(t)$, $f$ is a stochastic function (i.e. there is a noise term), and $t$ might be time or some other monotonic increasing ...