From 30936eea34d55dcd6ce09770dae9693f6759bb9a Mon Sep 17 00:00:00 2001 From: Yuchen Pei Date: Mon, 18 Feb 2019 10:12:40 +0100 Subject: fixed some typos --- posts/2019-02-14-raise-your-elbo.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) (limited to 'posts/2019-02-14-raise-your-elbo.md') diff --git a/posts/2019-02-14-raise-your-elbo.md b/posts/2019-02-14-raise-your-elbo.md index 5b789aa..0a3e6ed 100644 --- a/posts/2019-02-14-raise-your-elbo.md +++ b/posts/2019-02-14-raise-your-elbo.md @@ -38,7 +38,7 @@ slides](https://www.cs.tau.ac.il/~rshamir/algmb/presentations/EM-BW-Ron-16%20.pd (clear explanations of the connection between EM and Baum-Welch), Chapter 10 of [Bishop\'s book](https://www.springer.com/us/book/9780387310732) (brilliant -introduction to variational GMM) and Section 2.5 of [Sudderth\'s +introduction to variational GMM), Section 2.5 of [Sudderth\'s thesis](http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf) and [metacademy](https://metacademy.org). Also thanks to Josef Lindman Hörnlund for discussions. The research was done while working at KTH @@ -77,7 +77,7 @@ To this end, we can simply discard $D(q || p)$ in (1) and obtain: $$\log Z \ge L(w, q) \qquad (1.3)$$ -and keep in mind that the inequality becomes equality when +and keep in mind that the inequality becomes an equality when $q = {w \over Z}$. It is time to define the task of variational inference (VI), also known @@ -692,8 +692,9 @@ complicated, and we do not consider it this way here. Plugging in (9.1) we obtain the updates at E-step -$$r_{\ell i k} \propto \exp(\psi(\phi^{\pi_\ell}_k) + \psi(\phi^{\eta_k}_{x_{\ell i}}) - \psi(\sum_w \phi^{\eta_k}_w)). \qquad (10)$$ +$$r_{\ell i k} \propto \exp(\psi(\phi^{\pi_\ell}_k) + \psi(\phi^{\eta_k}_{x_{\ell i}}) - \psi(\sum_w \phi^{\eta_k}_w)), \qquad (10)$$ +where $\psi$ is the digamma function. Similarly, plugging in (9.3)(9.7)(9.9), at M-step, we update the posterior of $\pi$ and $\eta$: @@ -747,7 +748,7 @@ Both terms are infinite series: $$L(p, q) = \sum_{k = 1 : \infty} \mathbb E_{q(\theta_k)} \log {p(\theta_k) \over q(\theta_k)} + \sum_{i = 1 : m} \sum_{k = 1 : \infty} q(z_i = k) \mathbb E_{q(\theta)} \log {p(x_i, z_i = k | \theta) \over q(z_i = k)}.$$ -There are several solutions to deal with the infinities. One is to set +There are several ways to deal with the infinities. One is to fix some level $T > 0$ and set $v_T = 1$ almost surely (Blei-Jordan 2006). This effectively turns the model into a finite one, and both terms become finite sums over $k = 1 : T$. @@ -911,7 +912,7 @@ As an example, here\'s SVI applied to LDA: $(\phi^{\eta_k}_w)_{k = 1 : n_z, w = 1 : n_x}$: $$\phi^{\eta_k}_w = (1 - \rho_t) \phi^{\eta_k}_w + \rho_t \tilde \phi^{\eta_k}_w$$ -6. Increment $t$ and go back to Step 1. +6. Increment $t$ and go back to Step 2. In the original paper, $\rho_t$ needs to satisfy some conditions that guarantees convergence of the global parameters: -- cgit v1.2.3