Identifying Speculative Bubbles with an Infinite Hidden Markov Model
Identifying Speculative Bubbles with an Infinite Hidden Markov Model
Identifying Speculative Bubbles with an Infinite Hidden Markov Model
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ADirichlet Process <strong>an</strong>d Stick Breaking ProcessA.1 Dirichlet ProcessBefore introducing the Dirichlet process, the definition of the Dirichlet distribution is thefollowing:Definition The Dirichlet distribution is denoted by Dir(α), where α is a K-dimensionalvector of positive values. Each sample x from Dir(α) is a K-dimensional vector <strong>with</strong> x i ∈ (0, 1)∑<strong>an</strong>d K x i = 1. The probability density function isi=1p(x | α) =∑Γ( K α i ) K∏i=1K∏Γ(α i )i=1i=1x α i−1iA special case is the Beta distribution, where K = 2.∑Define α 0 = K α i <strong>an</strong>d X i as the ith element of the r<strong>an</strong>dom vector X from a Dirichleti=1distribution Dir(α). The r<strong>an</strong>dom variable X i has me<strong>an</strong> α iα 0<strong>an</strong>d vari<strong>an</strong>ce α i(α 0 −α i )α 2 0 (α 0+1) . Hence,we c<strong>an</strong> further decompose α into two parts: a shape parameter G 0 = ( α 1α 0, · · · , α Kα0) <strong>an</strong>d aconcentration parameter α 0 . The shape parameter G 0 represents the center of the r<strong>an</strong>domvector X <strong>an</strong>d the concentration parameter α 0 controls how close X is to G 0 .The Dirichlet distribution is conjugate to the multi-nominal distribution in the followingsense: ifX ∼ Dir(α),β = (n 1 , . . . , n K ) | X ∼ Mult(X),25