Decomposing a Sequence into Independent Subsequences Using
... corresponds to a connected component of the graph. The Dtest approach has a drawback: it can merge two independent components together even when there is only one false connection (not a connection but erroneously detected as a connection) between two vertices across two components. For instance, Fi ...
... corresponds to a connected component of the graph. The Dtest approach has a drawback: it can merge two independent components together even when there is only one false connection (not a connection but erroneously detected as a connection) between two vertices across two components. For instance, Fi ...
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
... it possible to detect either unusually rapid rating of products by a user (due to having a concentration of small time differences), or unusually regular patterns, such as rating products once every hour. Both of these patterns suggest bot-like or spammy behavior, which we would like to detect. More ...
... it possible to detect either unusually rapid rating of products by a user (due to having a concentration of small time differences), or unusually regular patterns, such as rating products once every hour. Both of these patterns suggest bot-like or spammy behavior, which we would like to detect. More ...
Linguistic knowledge about temporal data in Bayesian
... in which θA ∈ ΘA is a kA × 1 vector of unobservables 2) the prior density p(θA |A) 3) the vector of interest density (the posterior density) ...
... in which θA ∈ ΘA is a kA × 1 vector of unobservables 2) the prior density p(θA |A) 3) the vector of interest density (the posterior density) ...
指導教授：黃三益 博士 組員：B924020007 王俐文 B924020009
... 1. Compared with other data set, our data are not large enough. So we maybe get some troubles in the modeling process, such as outliers, skew distributions and missing values. 2. The values of the attribute named Media Exposure always show “Good”, so we can not estimate whether this attribute works ...
... 1. Compared with other data set, our data are not large enough. So we maybe get some troubles in the modeling process, such as outliers, skew distributions and missing values. 2. The values of the attribute named Media Exposure always show “Good”, so we can not estimate whether this attribute works ...
Supervised model-based visualization of high
... denotes the values of xi outside the target set, and xik is the value of attribute Xk in i data vector xi . Data vectors xi and xj are now considered similar if the corresponding predictive distributions are similar. It is easy to see that distance measures based on this type of similarity measures ...
... denotes the values of xi outside the target set, and xik is the value of attribute Xk in i data vector xi . Data vectors xi and xj are now considered similar if the corresponding predictive distributions are similar. It is easy to see that distance measures based on this type of similarity measures ...
How to Interpret SVD Units in Predictive Models?
... ABC Corp. (name changed due to the non-disclosure agreement with the party) is a national leader in telecommunication industry providing wide range of services such as television channels, high speed internet and home phone. They have a massive customer base with millions of households in the countr ...
... ABC Corp. (name changed due to the non-disclosure agreement with the party) is a national leader in telecommunication industry providing wide range of services such as television channels, high speed internet and home phone. They have a massive customer base with millions of households in the countr ...
EM algorithm
... which also has 5 parameters (two means, two variances, and a correlation). This is still a univariate distribution. The idea is for each observation Xi , there is a probability α that Xi comes from a N(µ1 , σ12 ), and a probability 1 − α that Xi comes from a N(µ2 , σ22 ). This is also different from ...
... which also has 5 parameters (two means, two variances, and a correlation). This is still a univariate distribution. The idea is for each observation Xi , there is a probability α that Xi comes from a N(µ1 , σ12 ), and a probability 1 − α that Xi comes from a N(µ2 , σ22 ). This is also different from ...
Bayesian Statistics and Belief Networks
... probability. • Bayesian Probability : A person’s degree of belief in event X. Personal probability. • Unlike classical probability, Bayesian probabilities benefit from but do not require repeated trials only focus on next event; e.g. probability Seawolves win next game? ...
... probability. • Bayesian Probability : A person’s degree of belief in event X. Personal probability. • Unlike classical probability, Bayesian probabilities benefit from but do not require repeated trials only focus on next event; e.g. probability Seawolves win next game? ...