Hypothesis Testing
... In t-test, we are interested in µ, but σ is unknown. σ makes things complicated, and is called nuisance parameter There can be many nuisance parameters. For example, in EFA or CFA, we want to confirm, say, 3 factors with 30 variables. Number of nuisance parameters are at least 90 (=30x3) and 30 ...
... In t-test, we are interested in µ, but σ is unknown. σ makes things complicated, and is called nuisance parameter There can be many nuisance parameters. For example, in EFA or CFA, we want to confirm, say, 3 factors with 30 variables. Number of nuisance parameters are at least 90 (=30x3) and 30 ...
Probability Distributions
... could have given rise to the observed finite data set. Indeed, any distribution p(x) that is nonzero at each of the data points x1 , . . . , xN is a potential candidate. The issue of choosing an appropriate distribution relates to the problem of model selection that has already been encountered in t ...
... could have given rise to the observed finite data set. Indeed, any distribution p(x) that is nonzero at each of the data points x1 , . . . , xN is a potential candidate. The issue of choosing an appropriate distribution relates to the problem of model selection that has already been encountered in t ...
Data Mining
... Decision Trees In decision theory (for example risk management), a decision tree is a graph of decisions and their possible consequences, (including resource costs and risks) used to create a plan to reach a goal. Decision trees are constructed in order to help with making decisions. A decision tr ...
... Decision Trees In decision theory (for example risk management), a decision tree is a graph of decisions and their possible consequences, (including resource costs and risks) used to create a plan to reach a goal. Decision trees are constructed in order to help with making decisions. A decision tr ...
SYSTEM BEHAVIOR ANALYSIS BY MACHINE LEARNING
... Bag of words We could view Bag of Words (BoW) as a way to quantize continuous feature space. For example, although color space is continuous in terms of optics, human eyes could only distinguish a certain number of colors, such as red, orange, blue, black, green, yellow, . . . . One way to describe ...
... Bag of words We could view Bag of Words (BoW) as a way to quantize continuous feature space. For example, although color space is continuous in terms of optics, human eyes could only distinguish a certain number of colors, such as red, orange, blue, black, green, yellow, . . . . One way to describe ...
Dorigo_CHIPP_part2
... • I will discuss the quantification of a signal’s significance later on. For now, let us only deal with our perception of it. • In our daily job as particle physicists, we develop the skill of seeing bumps –even where there aren’t any • It is quite important to realize a couple of things: 1) a likel ...
... • I will discuss the quantification of a signal’s significance later on. For now, let us only deal with our perception of it. • In our daily job as particle physicists, we develop the skill of seeing bumps –even where there aren’t any • It is quite important to realize a couple of things: 1) a likel ...
A Brief Reflection on Automatic Econometric Model
... Then two years later I needed to come up with a topic for my thesis. At an internship at the Ministry of Finance I was asked to investigate the price elasticities of motor fuels. Due to the abundant research available on this topic, it was interesting to compare my estimates (based on the extensive ...
... Then two years later I needed to come up with a topic for my thesis. At an internship at the Ministry of Finance I was asked to investigate the price elasticities of motor fuels. Due to the abundant research available on this topic, it was interesting to compare my estimates (based on the extensive ...
Crash course in probability theory and statistics – part 1
... Assume that the target variable is in this set, then we make decisions based on p(t | x, q ) = p( Ai | x, q ). Put in a different way: we use p(x,t | q) to classify x into one of k ...
... Assume that the target variable is in this set, then we make decisions based on p(t | x, q ) = p( Ai | x, q ). Put in a different way: we use p(x,t | q) to classify x into one of k ...
Abstract - Pascal Large Scale Learning Challenge
... label. This allows an exact calculation of the posterior probability of the models. Efficient search heuristic with super-linear computation time are proposed, on the basis of greedy forward addition and backward elimination of variables. 2.3. Compression-Based Model averaging Model averaging has be ...
... label. This allows an exact calculation of the posterior probability of the models. Efficient search heuristic with super-linear computation time are proposed, on the basis of greedy forward addition and backward elimination of variables. 2.3. Compression-Based Model averaging Model averaging has be ...
Dirichlet Enhanced Latent Semantic Analysis
... new data (the latter is problematic for PLSI). However, the parametric Dirichlet distribution can be a limitation in applications which exhibit a richer structure. As an illustration, consider Fig. 1 (a) that shows the empirical distribution of three topics. We see that the probability that all thre ...
... new data (the latter is problematic for PLSI). However, the parametric Dirichlet distribution can be a limitation in applications which exhibit a richer structure. As an illustration, consider Fig. 1 (a) that shows the empirical distribution of three topics. We see that the probability that all thre ...
LOYOLA COLLEGE (AUTONOMOUS), CHENNAI
... 6. Define dummy variable rule and explain the consequence of introducing m dummy variables for a categorical variable taking m categories in a multiple linear regression model with intercept 7. What is the use of a gains chart? 8. State the methods of hierarchical Clustering 9. Define kth principal ...
... 6. Define dummy variable rule and explain the consequence of introducing m dummy variables for a categorical variable taking m categories in a multiple linear regression model with intercept 7. What is the use of a gains chart? 8. State the methods of hierarchical Clustering 9. Define kth principal ...
Random function priors for exchangeable arrays with applications to
... if there exists a random probability measure Θ on X such that X1 , X2 , . . . | Θ ∼iid Θ, i.e., conditioned on Θ, the observations are independent and Θ-distributed. From a statistical perspective, Θ represents common structure in the observed data—and thus a natural target of statistical inference— ...
... if there exists a random probability measure Θ on X such that X1 , X2 , . . . | Θ ∼iid Θ, i.e., conditioned on Θ, the observations are independent and Θ-distributed. From a statistical perspective, Θ represents common structure in the observed data—and thus a natural target of statistical inference— ...
Fuzzy - Africa Geospatial Forum
... Identify many possible observations/variables that may be relevant to the problem Determine what subset of those observations is worthwhile to model Organize the observations into variables having mutually exclusive and collectively exhaustive states. Build a Directed Acyclic Graph that encodes the ...
... Identify many possible observations/variables that may be relevant to the problem Determine what subset of those observations is worthwhile to model Organize the observations into variables having mutually exclusive and collectively exhaustive states. Build a Directed Acyclic Graph that encodes the ...
