This page will contain summaries of the material covered in the lectures. You are expected to also
refer to the references provided for this
course to obtain a well rounded understanding of the subject.
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| Under construction - course starts in 2010.
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| Topic
| Note set
| Summary
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| Sets
| pdf
| The set notation required to discuss the fundamental concepts covered in this course
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| Probability
| pdf
| An introduction to probability in terms of Bayesian and Frequentist viewpoints.
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| Visualising and quantifying properties of data
| pdf
| Histograms and quantitative descriptions of data, including the notion
mean, variance, skew, FWHM, covariance and correlations.
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| Useful distributions
| some pdf
| Binomial, Poisson, Gaussian, Chi^2, Students-t distributions
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| Errors
| some pdf
| The nature of errors and the CLT, Combination of errors, Binomial error, Systematic errors, Blind Analysis
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| Hypothesis Testing
| some pdf
| Chi^2, Students-t, Toy Monte Carlo Method.
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| Confidence intervals
| some pdf
| One and two sided intervals, upper limits, testing the compatibility of results.
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| Minimisation (Fitting)
| some pdf
| Parameter determination using common test statistics, and an introduction to the techniques used in the minimisation process. Least-squares, Chi^2, Likelihood fits.
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| Multivariate analysis techniques
| some pdf
| Fisher discriminants, Artificial neural networks, decision trees, and the minimisation processes used in order to compute weight or coefficient sets required to classify events as a given species.
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