# 2 let us use mean field variational technique for

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2. Let us use mean-field variational technique for approximate inference. In particular, let us approximate the posterior by the following distribution: Q ( θ d , { z dn } N d n = 1 | γ d , { φ dn } N d n = 1 ) = braceleftBigg P ( θ d | γ d ) N d n = 1 ( φ dnk ) z dnk bracerightBigg (2) where φ dn = ( φ dn 1 , ··· , φ dnK ) is a variational multinomial distribution over topics for position n in the document d and γ d = ( γ d 1 , ··· , γ dK ) are the parameters of a variational Dirichlet distribution given by: P ( θ d | γ d ) = Γ ( k γ dk ) k Γ ( γ dk ) K k = 1 ( θ dk ) γ dk 1 (3) The graphical representation for the variational distribution is given in figure 1(b). 2

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(b) Mean field variational distribution for LDA M N w α θ β z γ θ z φ Μ Ν (a) LDA graphical representation Figure 1: Admixture model: graphical representation Starting with the results of the mean field variational inference given in Eq. (4) below, derive closed form expressions for the variational parameters γ dk and φ dnk . Q ( x ) = 1 Z exp ( Φ : X scope ( Φ ) E Q ( U ) [ ln Φ ( U Φ , x )]) (4) where Φ is a factor in the graphical model and U Φ = scope ( Φ ) X . In your derivation, you will use the following properties of the Dirichlet distribution: E Q ( θ d | γ d ) [ log θ dk ] = Ψ ( γ dk ) Ψ ( K k = 1 γ dk ) (5) where Ψ ( γ dk ) = d d γ dk log Γ ( γ dk ) is the digamma function. Prove the result above using the properties of the expo- nential family we discussed in class. (Hint: convert Dirichlet to its natural parametrization and use the property that the expected value of a sufficient statistic is equal to the first derivative of the log-partition function w.r.t the corresponding natural parameter.) 3. Now, let us consider the Logistic Normal admixture model, known in common parlance as the Correlated Topic Model. It differs from LDA in only that the symmetric Dirichlet prior with parameter α is replaced by a Logistic normal distribution, which is given as follows: P ( η d | μ , Σ ) = N ( μ , Σ ) (6) where η d = ( η d 1 , ··· , η dK ) with each η dk R . Each θ dk is a simple logistic transformation of η dk given by θ dk = exp ( η dk ) K k = 1 exp ( η dk ) (7) Using a logistic normal distribution as described above, allows us to capture correlations in topics, given by the matrix Σ .
• Fall '07
• CarlosGustin
• Normal Distribution, Probability theory, nd, Dirichlet distribution, Belief propagation, Chordal graph

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