Estimating the Same Quantities from Di erent Levels of Data:
... of aggregation. The statistical literatures bearing on events data have their own unique notation and specialized mathematical concepts|both of which do not exist in other areas of statistics that may be more familiar to political scientists. Thus, in the sections below, we begin by introducing a no ...
... of aggregation. The statistical literatures bearing on events data have their own unique notation and specialized mathematical concepts|both of which do not exist in other areas of statistics that may be more familiar to political scientists. Thus, in the sections below, we begin by introducing a no ...
ECML/PKDD 2004 - Computing and Information Studies
... Problem Definition • The pattern recognition task is to construct a model that captures an unknown input-output mapping on the basis of limited evidence about its nature. The evidence is called the training sample. We wish to construct the “best” model that is as close as possible to the true but u ...
... Problem Definition • The pattern recognition task is to construct a model that captures an unknown input-output mapping on the basis of limited evidence about its nature. The evidence is called the training sample. We wish to construct the “best” model that is as close as possible to the true but u ...
CS 561: Artificial Intelligence
... A CPT for Boolean Xi with k Boolean parents has 2k rows for the combinations of parent values Each row requires one number p for Xi =true (the number for Xi =false is just 1 - p) If each variable has no more than k parents, the complete network requires O(n ¢ 2k) numbers I.e., grows linearly with n, ...
... A CPT for Boolean Xi with k Boolean parents has 2k rows for the combinations of parent values Each row requires one number p for Xi =true (the number for Xi =false is just 1 - p) If each variable has no more than k parents, the complete network requires O(n ¢ 2k) numbers I.e., grows linearly with n, ...
Faithfulness in Chain Graphs: The Gaussian Case
... If a graph G contains an undirected (resp. directed) edge between two nodes v1 and v2 , then we write that v1 − v2 (resp. v1 → v2 ) is in G. If v1 → v2 is in G then v1 is called a parent of v2 . Let P aG (I) denote the set of parents in G of the nodes in I ⊆ V . When G is evident from the context, w ...
... If a graph G contains an undirected (resp. directed) edge between two nodes v1 and v2 , then we write that v1 − v2 (resp. v1 → v2 ) is in G. If v1 → v2 is in G then v1 is called a parent of v2 . Let P aG (I) denote the set of parents in G of the nodes in I ⊆ V . When G is evident from the context, w ...
Bat Call Identification with Gaussian Process Multinomial Probit
... Classification with GP models however is not amendable to analytical solutions and usually approximate inference methods are used. For binary classification, the Expectation Propagation (EP) algorithm has been shown to provide better approximation to the necessary integrals required for inference (K ...
... Classification with GP models however is not amendable to analytical solutions and usually approximate inference methods are used. For binary classification, the Expectation Propagation (EP) algorithm has been shown to provide better approximation to the necessary integrals required for inference (K ...
The Randomized Causation Coefficient
... decides that X → Y if ρ(P (X), | log(f 0 (X))|) < ρ(P (Y ), | log(g 0 (Y ))|), where ρ denotes Pearson’s correlation coefficient. IGCI decides Y → X if the opposite inequality holds, and abstains otherwise. The assumption here is that the cause random variable is independently generated from the map ...
... decides that X → Y if ρ(P (X), | log(f 0 (X))|) < ρ(P (Y ), | log(g 0 (Y ))|), where ρ denotes Pearson’s correlation coefficient. IGCI decides Y → X if the opposite inequality holds, and abstains otherwise. The assumption here is that the cause random variable is independently generated from the map ...
Estimation of Parameters and Fitting of Probability
... In this chapter, we discuss fitting probability laws to data. Many families of probability laws depend on a small number of parameters; for example, the Poisson family depends on the parameter λ (the mean number of counts), and the Gaussian family depends on two parameters, µ and σ . Unless the valu ...
... In this chapter, we discuss fitting probability laws to data. Many families of probability laws depend on a small number of parameters; for example, the Poisson family depends on the parameter λ (the mean number of counts), and the Gaussian family depends on two parameters, µ and σ . Unless the valu ...
Think-Aloud Protocols
... • Though other types of models (in particular knowledge engineering models) are amenable to this as well! ...
... • Though other types of models (in particular knowledge engineering models) are amenable to this as well! ...
DOTSE Report 169 NR 1345 ISSN 1174
... Consequently, traditional attrition-based models of combat described by Lanchester equations are becoming less relevant as a tool for analysing or predicting likely combat outcomes. Emphasis must instead be placed on analysing how manoeuvre affects combat. As increasingly lethal long-range weapon sy ...
... Consequently, traditional attrition-based models of combat described by Lanchester equations are becoming less relevant as a tool for analysing or predicting likely combat outcomes. Emphasis must instead be placed on analysing how manoeuvre affects combat. As increasingly lethal long-range weapon sy ...
Review of feature selection techniques in bioinformatics by Yvan
... Sequence analysis is one of the most traditional areas of bioinformatics. The problems that the programmer meets in this area can be divided in two types differing in the scope we are interested in. If we want to focus on general characteristics, to reason basing on statistical features of the whole ...
... Sequence analysis is one of the most traditional areas of bioinformatics. The problems that the programmer meets in this area can be divided in two types differing in the scope we are interested in. If we want to focus on general characteristics, to reason basing on statistical features of the whole ...
Miscellaneous Topics - McMaster Computing and Software
... to produce the most promising set • Assessment based on general characteristics of the data How about finding a subset of attributes that is enough to separate all the instances? • Expensive and overfitting Alternative: use one learning scheme(i.e. 1R) to select attributes and use the resulting attr ...
... to produce the most promising set • Assessment based on general characteristics of the data How about finding a subset of attributes that is enough to separate all the instances? • Expensive and overfitting Alternative: use one learning scheme(i.e. 1R) to select attributes and use the resulting attr ...
Towards Real-time Probabilistic Risk Assessment by
... previous data collection runs. For each news story we save the title, description, Globally Unique IDentifier (GUID) and last publication date information into a database. We collected data in this way for seven consecutive days. 2) Term Weighting: Terms that appear in each news title are considered ...
... previous data collection runs. For each news story we save the title, description, Globally Unique IDentifier (GUID) and last publication date information into a database. We collected data in this way for seven consecutive days. 2) Term Weighting: Terms that appear in each news title are considered ...
Kolker-Week1
... 1. (Question #2, page 30) For each of the following problem scenarios, decide if a solution would best be addressed with supervised learning, unsupervised clustering, or database query. As appropriate, state any initial hypothesis you would like to test. If you decide that supervised learning or uns ...
... 1. (Question #2, page 30) For each of the following problem scenarios, decide if a solution would best be addressed with supervised learning, unsupervised clustering, or database query. As appropriate, state any initial hypothesis you would like to test. If you decide that supervised learning or uns ...