44 lines
1.1 KiB
Plaintext
44 lines
1.1 KiB
Plaintext
---
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title: Scratch Page
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toc: no
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format: markdown
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...
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# Math Stuff
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On observation of $\mathcal{D}$, the *likelihood* of hypothesis
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$\mathcal{R}_{\alpha}$ is
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$\mathit{P}(\mathcal{D}|\mathcal{R}_{\alpha})$.
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## Fingerprint Variance
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Additionally, we associate a collective **RSS Variance**
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$\sigma_{F_s}$ with each fingerprint, which is a weighed average of
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the RSS values of each of the vector elements using $C_i$ as the
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weight. It is calculated in this manner:
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$$
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\sigma_{F_s} = \frac{\sum_{i \in F_s} \sigma_i\, C_i}
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{\sum_{i \in F_s} C_i}
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$$
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# Bayesian Regression
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First, specify a set of probabilistic models of the data.
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Let a member of this set be denoted by $\mathcal{R}_\alpha$
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$\mathcal{R}_\alpha$ has a *prior* probability $P(\mathcal{H}_\alpha)$
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On observation of $\mathcal{D}$, the *likelihood* of hypothesis
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$\mathcal{R}_{\alpha}$ is
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$\mathit{P}(\mathcal{D}|\mathcal{R}_{\alpha})$.
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The *posterior* probability of $\mathcal{R}_{\alpha}$ is then given by
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$\mathit{P}(\mathcal{H}_{\alpha})\mathit{P}(\mathcal{D}|\mathcal{H}_{\alpha})$
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This follows from **Bayes' Theorem** which says
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$$
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P(A|B) = \frac{P(B | A)\, P(A)}{P(B)}
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$$ |