# Tagged Questions

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### Confusion: different definitions of MAP estimation in Graphical Models (MRFs)

The "classical" MAP estimation: $$\hat\theta = \arg\max_{\theta}P(\theta|\mathbf{x})$$ where $\mathbf{x}$ are the observations and $\theta$ are the parameters. In this book chapter (page 6, second ...
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### Predicting the edges of a graph

I have a dataset of paired relations, indicating whether $a$ is in relation with $b$. It is better to consider this dataset as a graph where each node has a numerical value as its feature. Let's say ...
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### Is it possible that grid search would fail in two dimensional feature space?

Grid search suffers from the curse of dimensionality. But is there any case(any hypothetical distribution of data) in a two dimensional feature space where the data’s binary classification using Grid ...
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### Are empirical risk minimization and M-estimators the same thing?

Would it be true to say that Empirical Risk Minimization and M-Estimation are the same thing?
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### 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 ...
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### 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 ...
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### componentwise boosting based on fisher scoring

componentwise boosting dates back at least to Bühlman and Yu (2003), where in each boosting iteration a set of base-learners (e.g. simple linear models) depending on a subset of the covariates are ...
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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 ...
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### 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 ...
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### difference in training and testing procedure of model

Can anyone please tell me the difference in training and testing of a model. I have developed 5/6 different single pass online learning algorithm (ets, ets+, evolving fuzzy modelling, SOFNN, ...
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### Bootstrapping Methodology

I have this bootstrap setting: Given $n$ vector-valued statistics (each statistic is a vector) of dimension $r \times 1$, I generated $1000$ bootstrap samples and then generated an estimate of each ...
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### Statistics for machine learning, papers to start?

I have a background in computer programming and elementary number theory, but no real statistics training, and have recently "discovered" that the amazing world of a whole range of techniques is ...
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### Machine learning predicted value

When we fit a generalized linear regression (e.g., logistic regression, gamma regression) we are estimating the population average Y given the predictors $X$ ( i.e., $E(Y | X)$ ). When we fit a ...
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### having trouble applying hidden markov models to my game [duplicate]

Possible Duplicate: having trouble applying hidden markov/machine learning models Happy New Year! I’m having a problem applying hidden Markov models to a game I’m building to learn about ...
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### Adjusting existing algorithm - likelihood for presence-only data

Logistic regression fits a model that predicts a binary variable whilst performing a logit transformation of the linear combination (LC) of predictors: 1/1 + exp(-LC). I have a working machine ...
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### Why would concatenating feature vectors lead to better estimates?

I wish to estimate the state of a system from two separate and disparate observations. A simple approach that I have seen in some literature is to combine the feature vectors (observations) by simply ...
Can anyone recommend any machine learning techniques for time series estimation? I have a series of times $t_{1}...t_{n}$, each having a set of associated features $f_{1}...f_{m}$, and a value $x$. ...