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# Zero-Inflated Binomial Distribution
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The Zero-Inflated Binomial (ZIB) distribution is a discrete probability distribution used to model count data characterized by an excess of zeros. It combines two components: a standard Binomial distribution and an additional mechanism that increases the probability of observing zero outcomes.
This distribution is particularly useful for scenarios where the data exhibit more zeros than what the Binomial model alone would predict. For example, in a study of the number of doctor visits in a year, many individuals might not visit a doctor at all, resulting in a higher frequency of zero counts than expected under a traditional Binomial model.
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# Zero-Inflated Negative Binomial Distribution
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The Zero-Inflated Negative Binomial (ZINB) distribution is a discrete probability distribution used to model count data characterized by an excess of zeros. It combines two components: a NegativeBinomial component, which describe count values and a zero-inflation component, which accounts for the extra zeros.
## Key properties and parameters
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# Zero-Inflated Poisson Distribution
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The Zero-Inflated Poisson (ZIP) distribution is a discrete probability distribution used to model count data characterized by an excess of zeros. It combines two components: a standard Poisson distribution and an additional mechanism that increases the probability of observing zero outcomes.
This distribution is particularly useful for scenarios where the data exhibit more zeros than what the Poisson model alone would predict. For example, in ecological studies, when researchers survey multiple habitats for a particular species, they often encounter many sites with zero observations (typically due to unsuitable habitat conditions) alongside a smaller number of sites where the species is observed in varying counts.