3 votes

What is the definition of a heuristic function in the BayesChess paper?

By far the most common form of heuristic evaluation functions for Chess-playing (or, really, any game-playing) agents are simple linear functions. At least when we're talking about handcrafted ...
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  • 9,316
2 votes

What is the definition of a heuristic function in the BayesChess paper?

Heuristics can be understood aas rules. Typically heuristics are thought of as problem-specific strategies. Expert systems were an early form of AI that utilized rules-based decisions. In a game-...
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  • 6,077
2 votes
Accepted

What does all the formula and pictures mean?

According to the provided article, $$ \begin{equation} \tag{1} P(D| {\mathcal{E}};\ \theta ) \end{equation} $$ is a probability of disease $D$ given findings $\mathcal{E}$, and a model $\theta$ that ...
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2 votes

Problem with Proposition 1 of Google Deepmind's 'Weight uncertainty in Neural Networks'

we start with $\frac{\partial}{\partial \theta} \mathbb E_{q(\mathbf w\mid\theta)}[f(\mathbf w, \theta)]$ using definition of expectation for continuous case: $\mathbb E[X] = \int xp(x) dx$ ...
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  • 2,216
1 vote

What is the difference between a Bayesian Network and a Markov Chain?

The main difference between a Bayesian network and a Markov chain is not that a Markov Chain is not directional, it is that the graph of the Bayesian network is not trivial whereas the graph of a ...
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1 vote

Why do Bayesian algorithms work well with small datasets?

The main reason should be that Bayesian algorithms naturally incorporate a form of regularisation (the prior), so they should be less prone to over-fitting the small dataset. Of course, the choice of ...
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  • 33k
1 vote

How do I determine the most appropriate classifier for a certain problem?

This is one of the main skills that separates someone with a deep understanding of, and experience in, machine learning learning, from a neophyte. There are several approaches: Try several methods, ...
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1 vote

Is it possible to compute $P( F \mid S )$ given $P(F \mid S,A)$ and $P(F \mid S, \lnot A)$ in Bayesian network?

This is a bit of a puzzle but you can compute a reasonable narrow limit even without knowing whether or not $P(S,A) = P(S) P(A)$. Start with the contingency table relating $P(S, A)$, $P(S,\neg A)$, $...
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1 vote

Why is exact inference in a Bayesian network both NP-hard and P-hard?

It's not completely clear from your question, but it looks like you want to prove that exact inference in a Bayesian Network is both NP-Hard and P-Hard. It appears that you have proven that it is NP-...
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1 vote

Are Bayesian networks important to learn in 2018?

The chapters for Bayesian Networks are: Quantifying Uncertainty Probabilistic Reasoning Dynamic Bayesian don't forget: Naive Bayes, hidden variables, Markov Maybe helpful: . Are We Going in the ...
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