SPM5 – New Features
... A major new feature of SPM5 is the functionality to analyze epoched EEG and MEG data. This functionality can be divided into two components: (i) preprocessing and (ii) statistical analysis. Preprocessing contains the usual steps; e.g. epoching, filtering, artifact detection, time-frequency decomposi ...
... A major new feature of SPM5 is the functionality to analyze epoched EEG and MEG data. This functionality can be divided into two components: (i) preprocessing and (ii) statistical analysis. Preprocessing contains the usual steps; e.g. epoching, filtering, artifact detection, time-frequency decomposi ...
Conditional simulation of diamond deposits
... gullies or small depressions in the rough bedrock. Therefore, when taking samples of such deposits, discrete distributions of the number of stones counted in each sample yield an extreme skewness. Most samples have no stones, whereas samples containing a few hundred stones are not uncommon. A method ...
... gullies or small depressions in the rough bedrock. Therefore, when taking samples of such deposits, discrete distributions of the number of stones counted in each sample yield an extreme skewness. Most samples have no stones, whereas samples containing a few hundred stones are not uncommon. A method ...
The shape of distributions (2 fragments)
... k is not only the mean but also the mode of the distribution. It is also known that the probability Pk of flipping exactly k heads is smallest when p1 = . . . = pn = k/n. So we might expect that when we ‘average’ two of the pi ’s, that is, when we replace pi and pj by p0i = (1 − t)pi + tpj and p0j = ...
... k is not only the mean but also the mode of the distribution. It is also known that the probability Pk of flipping exactly k heads is smallest when p1 = . . . = pn = k/n. So we might expect that when we ‘average’ two of the pi ’s, that is, when we replace pi and pj by p0i = (1 − t)pi + tpj and p0j = ...
down
... Generative Model • or forward model is a probabilistic mapping from causes to observed consequences (data). It is usually specified in terms of the likelihood of getting some data given their causes (parameters of a model) and priors on the parameters ...
... Generative Model • or forward model is a probabilistic mapping from causes to observed consequences (data). It is usually specified in terms of the likelihood of getting some data given their causes (parameters of a model) and priors on the parameters ...
Handout on Mixtures of Densities and Distributions
... j=1 Ωj . Suppose that when an individual is sampled randomly from the full population Ω, the probability with which that individual falls into Ωj is wj , but that it is not necessarily known to the experimenter which subpopulation Ωj contains that individual. (The probabilities wj are assumed to be ...
... j=1 Ωj . Suppose that when an individual is sampled randomly from the full population Ω, the probability with which that individual falls into Ωj is wj , but that it is not necessarily known to the experimenter which subpopulation Ωj contains that individual. (The probabilities wj are assumed to be ...
Three Approaches to Probability Model Selection
... Section 4 presents a third approach: to maximize the ratio of the likelihood of the data, as an accuracy measure, to a computational cost measure that increases with the number of model parameters [Poland and Shachter 1993]. For a certain form of the cost function, this approach is shown to be equiv ...
... Section 4 presents a third approach: to maximize the ratio of the likelihood of the data, as an accuracy measure, to a computational cost measure that increases with the number of model parameters [Poland and Shachter 1993]. For a certain form of the cost function, this approach is shown to be equiv ...
