Introduction To Probability

2017-03-21

Probability is an important statistical and mathematical concept to understand. In simple terms – probability refers to the chances of possible outcome of an event occurring within the domain of multiple outcomes. Probability is indicated by a whole number – with 0 meaning that the outcome has no chance of occurring and 1 meaning that the outcome is certain to happen. So it is mathematically represented as P(event) = (# of outcomes in event / total # of outcomes). In addition to understanding this simple thing, we will also look at a basic example of conditional probability and independent events.

It’s a good intro to a critical topic in statistics.  If I would add one thing to this, it would be to state that probability is always conditional upon something.  It’s fair to write something as P(Event) understanding that it’s a shortcut, but in reality, it’s always P(Event | Conditions), where Conditions is the set of assumptions we made in collecting this sample.

Neural Nets On Spark

2017-06-28

Nisha Muktewar and Seth Hendrickson show how to use Deeplearning4j to build deep learning models on Hadoop and Spark: Modern convolutional networks can have several hundred million parameters. One of the top-performing neural networks in the Large Scale Visual Recognition Challenge (also known as “ImageNet”), has 140 million parameters to train! These networks not only […]

Linear Prediction Confidence Region Flare-Out

2017-06-26

John Cook explains why the confidence region of a tracked object flares out instead of looking conical (or some other shape): Suppose you’re tracking some object based on its initial position x0 and initial velocity v0. The initial position and initial velocity are estimated from normal distributions with standard deviations σx and σv. (To keep […]

March 2017
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