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How to Build a “Fake News” Classification Model.

How to Build a ̶...

“A lie gets halfway around the world before the truth has a chance to get its pants on.” – Winston Churchill Since the 2016 presidential election, one topic dominating political discourse is the issue of “Fake News”. A number of political pundits claim that the rise of  significantly biased and/or untrue news influenced the election, though a study by researchers […]

Bayesian Deep Learning Part II: Bridging PyMC3 and Lasagne to build a Hierarchical Neural Network

Bayesian Deep Learni...

Thomas originally posted this article here at http://twiecki.github.io  Not to long ago, I blogged about Bayesian Deep Learning with PyMC3 where I built a simple hand-coded Bayesian Neural Network and fit it on a toy data set. Today, we will build a more interesting model using Lasagne, a flexible Theano library for constructing various types of Neural […]

Bayes’s Theorem is Not Optional

Bayes’s Theore...

Abstract: I present a probability puzzle, the Rain in Seattle Problem, and use it to explain differences between the Bayesian and frequentist interpretations of probability, and between Bayesian and frequentist statistical methods.  Since I am trying to clear up confusion, I try to describe the alternatives without commenting on their pros and cons. Introduction Conversations […]

I Probably won’t Win the Great Bear Run

I Probably won’...

This is an update to that article I wrote about my chances of winning my age group in a 5K.   Almost every year since 2008 I have participated in the Great Bear Run, a 5K road race in Needham MA. I usually finish in the top 30 or so, and in my age group […]

Stats Can’t Make Modeling Decisions

Stats Can’t Ma...

Here’s a question that appeared recently on the Reddit statistics forum: If effect sizes of coefficient are really small, can you interpret as no relationship?  Coefficients are very significant, which is expected with my large dataset. But coefficients are tiny (0.0000001). Can I conclude no relationship? Or must I say there is a relationship, but […]

Probability is hard: part 4

Probability is hard:...

This is the fourth part of a series of posts about conditional probability and Bayesian statistics. In the first article, I presented the Red Dice problem, which is a warm-up problem that might help us make sense of the other problems. In the second article, I presented the problem of interpreting medical tests when there is uncertainty about […]

Probability is hard: part three

Probability is hard:...

This is the third part of a series of posts about conditional probability and Bayesian statistics. In the first article, I presented the Red Dice problem, which is a warm-up problem that might help us make sense of the other problems. In the second article, I presented the problem of interpreting medical tests when there is uncertainty […]

Allay Doubt in Bayesian Inference with MCMC & Metropolis-Hastings

Allay Doubt in Bayes...

Bayesian inference is a statistical method used to update a prior belief based on new evidence, an extremely useful technique with innumerable applications. Uncertainty about probabilities that are hard to quantify is one of the challenges of Bayesian inference, but there is a solution that is exciting for its cross-disciplinary origins and the elegant chain of […]

Probability is hard, part two

Probability is hard,...

If you read the previous post, you know that my colleague Sanjoy Mahajan and I have been working on a series of problems related to conditional probability and Bayesian statistics.  In the previous article, I presented the Red Dice problem, which is relatively simple.  I posted it here because it presents four different versions of the […]