# how to do variational bayesian inference in matlab

Variational Bayesian inference is a powerful tool in machine learning that allows you to approximate complex Bayesian models that are otherwise intractable. Here's how to perform variational Bayesian inference in MATLAB:

1. Define your model: You need to write down your probabilistic model in terms of likelihood and priors. For example, suppose we have data `X` that we model as a mixture of gaussians with unknown means and variances. Our likelihood would be:
```main.m```p(X | Z, mu, sigma) = prod_i [ sum_k [ Z_ik * N(X_i | mu_k, sigma_k) ] ]
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where `Z` are our latent variables indicating the mixture component for each data point. `mu` and `sigma` are our unknown means and variances, parameterizing our gaussian mixture model. We will assume a Gaussian prior on our means and an inverse-gamma prior on our variances.

1. Define your variational distribution: We need to choose a family of distributions to approximate our true posterior over the latent variables. A popular choice is the mean-field approximation, which assumes that our posterior over the latent variables factorizes into independent distributions:
```main.m```q(Z, mu, sigma) = q(Z) * q(mu) * q(sigma)
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We can choose a Gaussian distribution for `q(mu)` and an inverse-gamma distribution for `q(sigma)`.

1. Define the variational objective: Our goal is to find the set of parameters that minimize the KL-divergence between our true posterior and our approximate posterior. This is equivalent to maximizing the evidence lower bound (ELBO):
```main.m```L(q) = E_q[ log p(X,Z,mu,sigma) - log q(Z,mu,sigma) ]
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We can optimize this objective using gradient-based optimization methods such as stochastic gradient descent (SGD).

1. Run the optimization: With our ELBO objective defined, we can run an optimization routine to find the optimal parameters of our variational distribution that maximize the ELBO. In MATLAB, we can use the optimization toolbox to perform the optimization. Here's an example code snippet using the fminunc function:
```main.m```% Define your model and data

% Define your initial guess for the variational distribution
mu0 = % Some initial guess for the mean
sigma0 = % Some initial guess for the variance

% Define your ELBO objective function
ELBO = @(params) compute_ELBO(X, params);

% Run the optimization
params_opt = fminunc(ELBO, [mu0; sigma0]);

% Extract the optimal parameters
mu_opt = params_opt(1);
sigma_opt = params_opt(2);
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You will need to implement a function `compute_ELBO` that computes the ELBO for a given set of parameters. This function should evaluate the expected log-likelihood and the entropy term of the ELBO using Monte Carlo methods.

And that's it! With these steps, you should be able to perform variational Bayesian inference on your model in MATLAB.

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