3795 search results for "gis"

Predicting claims with a bayesian network

November 19, 2013
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Predicting claims with a bayesian network

Here is a little Bayesian Network to predict the claims for two different types of drivers over the next year, see also example 16.16 in . Let's assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%,...

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Binomial regression model

November 18, 2013
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Binomial regression model

Most of the time, when we introduce binomial models, such as the logistic or probit models, we discuss only Bernoulli variables, . This year (actually also the year before), I discuss extensions to multinomial regressions, where  is a function on some simplex. The multinomial logistic model was mention here. The idea is to consider, for instance with three possible classes the following...

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Success rates for EPSRC Fellowships

November 18, 2013
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Success rates for EPSRC Fellowships

Email I was recently at a presentation where the success rates for EPSRC fellowships were given by theme. The message of the talk was that Engineering fellowships were under-subscribed and so we should all be preparing our applications. But just because a theme is under-subcribed doesn’t mean that you’ve got a better chance of getting

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Some Options for Testing Tables

November 18, 2013
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Some Options for Testing Tables

Contingency tables are a very good way to summarize discrete data.  They are quite easy to construct and reasonably easy to understand. However, there are many nuances with tables and care should be taken when making conclusions related to the data. Here are just a few thoughts on the topic. Dealing with sparse data On

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Evaluating Quandl Data Quality

November 15, 2013
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Quandl has indexed millions of time-series datasets from over 400 sources. All of Quandl’s datasets are open and free. This is great news but before performing any backtest using Quandl data, I want to compare it with a trusted source: Bloomberg for the purpose of this post. I will focus only on daily Futures data here

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Visualizing neural networks in R – update

November 14, 2013
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Visualizing neural networks in R – update

In my last post I said I wasn’t going to write anymore about neural networks (i.e., multilayer feedforward perceptron, supervised ANN, etc.). That was a lie. I’ve received several requests to update the neural network plotting function described in the original post. As previously explained, R does not provide a lot of options for visualizing

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Sixth Torino R net meeting

November 14, 2013
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Sixth Torino R net meeting

Sixth Torino R net meeting on 21 Nov 2013, Campus Luigi Einaudi, Università degli Studi di Torino, will have three presentations The importance of R in agrometeorology: a case study, Federico Spanna, Regione Piemonte Settore Fitosanitario; Claudio Cassardo, Università di Torino Dipartimento … Continue reading →

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…start using R, from scratch!

November 14, 2013
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…start using R, from scratch!

Some time ago, since I was able to use R by myself, have found some fellows and other people who wanted to learn R as well. Then I pointed them to help pages, to CRAN repositories… but in some cases … Sigue leyendo →

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Trying to reduce the memory overhead when using mclapply

November 14, 2013
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Trying to reduce the memory overhead when using mclapply

I am currently trying to understand how to reduce the memory used by mclapply. This function is rather complicated and others have explained the differences versus parLapply (A_Skelton73, 2013; Read more »

Iterators in R

November 13, 2013
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According to Wikipedia, an iterator is “an object that enables a programmer to traverse a container”. A collection of items (stashed in a container) can be thought of as being “iterable” if there is a logical progression from one element to the next (so a list is iterable, while a set is not). An iterator

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