data mining methods for gis analysis of seismic vulnerability
... probability associated with it, a real number r ∈ [0,1] . The goal is to find the subsets of nearby points, clusters, which share the same Cr, or at least clusters with minimum impurity, i.e. most of the cluster members should belong to the same class or have close r values. A straightforward approa ...
... probability associated with it, a real number r ∈ [0,1] . The goal is to find the subsets of nearby points, clusters, which share the same Cr, or at least clusters with minimum impurity, i.e. most of the cluster members should belong to the same class or have close r values. A straightforward approa ...
Logistic Regression
... In terms of predicting power, there is a debate over which technique performs better, and there is no clear winner. As stated before, the general view is that Logistic Regression is preferred for binomial dependent variables, while discriminant is better when there are more than 2 values of the depe ...
... In terms of predicting power, there is a debate over which technique performs better, and there is no clear winner. As stated before, the general view is that Logistic Regression is preferred for binomial dependent variables, while discriminant is better when there are more than 2 values of the depe ...
Differences-in-Differences and A (Very) Brief Introduction
... Consistent estimate of parameters from OLS Use these parameters to construct fitted value ...
... Consistent estimate of parameters from OLS Use these parameters to construct fitted value ...
Document
... • The first factor is the evidence for hi, while the second factor Is our subjective prior over the space of hypotheses. • If we neglect the second term, we have a maximum likelihood solution. ...
... • The first factor is the evidence for hi, while the second factor Is our subjective prior over the space of hypotheses. • If we neglect the second term, we have a maximum likelihood solution. ...
LO3120992104
... (EM) [11] is a probabilistic clustering method. It is used to find out the maximum likelihood for the parameters of the probability distribution in the model. It groups traffic based on the similar properties into distinct application types. Based on the feature, the flows are grouped into small num ...
... (EM) [11] is a probabilistic clustering method. It is used to find out the maximum likelihood for the parameters of the probability distribution in the model. It groups traffic based on the similar properties into distinct application types. Based on the feature, the flows are grouped into small num ...
A Network Algorithm to Discover Sequential Patterns
... seems easier than using association rules or using the link analysis. Once the branches of items are known the user can easily decide what is the next-item for each customer. In Data Mining the time complexity of the algorithms is very important. To discover sequence patterns we propose the Ramex al ...
... seems easier than using association rules or using the link analysis. Once the branches of items are known the user can easily decide what is the next-item for each customer. In Data Mining the time complexity of the algorithms is very important. To discover sequence patterns we propose the Ramex al ...
Data Mining in Market Research
... • Selected with replacement, same # of instances – Can use parametric or non-parametric bootstrap ...
... • Selected with replacement, same # of instances – Can use parametric or non-parametric bootstrap ...
Robust statistics: a method of coping with outliers
... the tests may mislead if two or more outliers are present. Secondly, we have to decide whether to exclude the outlier during the calculation of further statistics. This raises the contentious question of when it is justifiable to exclude outliers. Robust statistics provides an alternative procedure, ...
... the tests may mislead if two or more outliers are present. Secondly, we have to decide whether to exclude the outlier during the calculation of further statistics. This raises the contentious question of when it is justifiable to exclude outliers. Robust statistics provides an alternative procedure, ...
KClustering
... K-means and K-harmonic means are two center-based algorithm that have been developed to solve this problem. K-means (KM) is a popular algorithm that was first presented over three decades ago [1]. The criterion it uses minimizes the total mean-squared distance from each point in N to that point’s cl ...
... K-means and K-harmonic means are two center-based algorithm that have been developed to solve this problem. K-means (KM) is a popular algorithm that was first presented over three decades ago [1]. The criterion it uses minimizes the total mean-squared distance from each point in N to that point’s cl ...
Bayesian Inference for Stochastic Epidemics in
... limited application to the modelling of specific diseases. However, our objective is to develop methods of statistical inference, and it seems sensible to do so with a basic model before moving on to more complex situations. Furthermore, our focus in this paper is towards moderately-sized datasets, f ...
... limited application to the modelling of specific diseases. However, our objective is to develop methods of statistical inference, and it seems sensible to do so with a basic model before moving on to more complex situations. Furthermore, our focus in this paper is towards moderately-sized datasets, f ...
Lecture Scribe on Machine Learning(week-1)
... our data set, you know, we don’t know in advance who is in market segment one, who is in market segment two, and so on. But we have to let the algorithm discover all this just from the data. Finally, it turns out that Unsupervised Learning is also used for surprisingly astronomical data analysis and ...
... our data set, you know, we don’t know in advance who is in market segment one, who is in market segment two, and so on. But we have to let the algorithm discover all this just from the data. Finally, it turns out that Unsupervised Learning is also used for surprisingly astronomical data analysis and ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.