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Image denoising with Gibbs sampler

The key is that you can partition $P(X = x)$ into a part that depends on $x_a$ and another part that does not depend on $x_a$ $$P(X=x) = \frac{1}{C(\alpha \beta)} \exp\left\{ \alpha \sum_{a \in L} x_a ...
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Uniform ergodicity of a Gibbs sampler

The integrating density in $$p_{ \beta^{(1)} | \beta^{(0)} } ( w |y) = \int_{(0, \infty )^{10}} p_{ \beta^{(1)} | \lambda^{(1)} , \beta^{(0)} } ( w| z ,y ) \ p_{ \lambda^{(1)} | \beta^{(0)} } ( z | y) ...
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