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Bag of Timestamps: A Simple and Efficient Bayesian Chronological Mining

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Tomonari Masada
Tomonari Masada
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[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],t “ test” t t “ test” “ group” “ group” “ group” “ effect” “ space” “ space” t t −1 t −1 t +1 t +1
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
θ α z t z w β φ γ ψ
[object Object],[object Object],[object Object]
Topics over Time Bag of Timestamps Modification of LDA (Beta distribution for continuous timestamps) Modification of LDA (Dirichlet-multinomial for discrete timestamps ) O(NK) time, O(N) space N: number of word tokens O((N+L)K) time, O(N+L) space L: sum of timestamp array lengths Non-Bayesian term in updating formula for Gibbs sampling Additional input parameter for timestamp array lengths
θ α z t z w β φ ψ 1 , ψ 2
 
 
 
 
 
 
 
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 

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Bag of Timestamps: A Simple and Efficient Bayesian Chronological Mining

  • 1.  
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  • 5. θ α z t z w β φ γ ψ
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  • 7. Topics over Time Bag of Timestamps Modification of LDA (Beta distribution for continuous timestamps) Modification of LDA (Dirichlet-multinomial for discrete timestamps ) O(NK) time, O(N) space N: number of word tokens O((N+L)K) time, O(N+L) space L: sum of timestamp array lengths Non-Bayesian term in updating formula for Gibbs sampling Additional input parameter for timestamp array lengths
  • 8. θ α z t z w β φ ψ 1 , ψ 2
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