The probability of a sequence of n independent Bernoulli trials can be easily expressed as $p(x_1,...,x_n|p_1,...,p_n)=\prod_{i=1}^np_i^{x_i}(1-p_i)^{1-x_i}$ but what if the trials are not independent? how would one express the probability to capture the dependence?
Dependent Bernoulli trials
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probability
combinatorics
statistics
probability-distributions
machine-learning
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0Related: http://math.stackexchange.com/questions/52137/multivariate-normal-and-multivariate-bernoulli/52321#52321 – 2012-12-27
1 Answers
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The most flexible structure is the one that assings to all possible $n$ binary vectors $\left( x_1, \ldots, x_n \right)$ a probabilty $P \left[ x_1 = i_1, \ldots, x_n = i_n \right] = p_{i_1, \ldots, i_n}$ such that $ \sum_{i_1 = 0}^1 \cdots \sum_{i_n = 0}^1 p_{i_1, \ldots, i_n} = 1$ Thus, you have to specify $2^n - 1$ parameters (This is much more complicated than your i.i.d. case where you specify $n$ parameters $p_1, \ldots, p_n$.)
There are many ways to simplify the problem.
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0A random vector of dependent Bernoulli RVs, each with its own parameter $P(X_i=1|X_1,\ldots,X_{i-1},X_{i+1},\ldots,X_n)=p_i$ – 2012-12-27