# FUN in S&T

## Tuesday, June 28, 2011

## Saturday, June 25, 2011

### Math Forum - Ask Dr. Math

inf means "infimum," or "greatest lower bound." This is slightly different from minimum in that the greatest lower bound is defined as: x is the infimum of the set S [in symbols, x = inf (S)] iff: a) x is less than or equal to all elements of S b) there is no other number larger than x which is less than or equal to all elements of S. Basically, (a) means that x is a lower bound of S, and (b) means that x is greater than all other lower bounds of S.

## Friday, June 24, 2011

## Thursday, June 23, 2011

### Data mining Blog, Preprocessing, Normalization

Before we normalize the data set, we need to check for outliers

There are two possible ways to handle outliers in the data

1. Ignore them, remove from the data set (or)

2. Reassign the value to of appropriate upper or lower threshold.

## Thursday, June 16, 2011

## Thursday, June 9, 2011

## Thursday, June 2, 2011

## Monday, May 9, 2011

## Friday, May 6, 2011

## Monday, May 2, 2011

### Stat 375 – Inference in Graphical Models

# Stat 375 – Inference in Graphical Models

Graphical models are a unifying framework for describing the statistical relationships between large collections of random variables. Given a graphical model, the most fundamental (and yet highly non-trivial) task is compute the marginal distribution of one or a few such variables. This task is usually referred to as ‘inference’. The focus of this course is on sparse graphical structures, low-complexity inference algorithms, and their analysis. In particular we will treat the following methods: variational inference; message passing algorithms; belief propagation; generalized belief propagation; survey propagation; learning. Applications/examples will include: Gaussian models with sparse inverse covariance; hidden Markov models (Viterbi and BCJR algorithms, Kalman filter); computer vision (segmentation, tracking, etc); constraint satisfaction problems; machine learning (clustering, classification); communications. |

## Sunday, May 1, 2011

## Thursday, April 28, 2011

## Wednesday, April 27, 2011

### Classifier Showdown « Synaptic

**naive bayesian classifiers**,

**s**

*upport vector machines*

**and modular multilayer perceptron neural networks.**