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My buddy and I are using Monte Carlo Markov Chains to make a heat map - in the interest of rigour, and out of curiosity:

"What are the problems & biases that stem from the mathematics behind using Monte Carlo Markov Chain to make a heatmap, and what alternatives or corrective measures would you use to remedy this?"

I'm finding it difficult to find a comparison of different predictive algorithms ! What I want to know is what biases are induced by the mathematics behind this algorithm, and which simpler/more complex proceedures would one use to correct for these.

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    This question is a little too broad to answer well. Perhaps you could edit it to explain what kind of data you have, what statistical model you are using to make a "heat map," and what that map is intended to represent. – whuber Jan 18 '14 at 22:35

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