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  "Title": "Bayesian Dynamic Models for Count Time Series",
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  "Description": "Fits Bayesian state-space models for count time series\nusing a latent log-rate (Poisson), latent logit (binomial) or\nlatent additive-log-ratio (multinomial choice counts)\nformulation. Each latent trajectory follows a first-order\nrandom walk or a stationary AR(1) process and is sampled by\nMetropolis-within-Gibbs using the implied Gaussian Markov\nrandom field full conditionals. The latent increments can be\nGaussian, Student-t, a finite scale mixture of normals, or\nfollow a stochastic volatility process, and the Poisson and\nbinomial families support zero inflation. It implements and\nextends the methodology of Zens and Bijak (2026)\n<doi:10.1214/26-AOAS2171>.",
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      "title": "Weekly Mediterranean crossings (Mediterranean example)",
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      "title": "Plot observed versus fitted values",
      "topics": [
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      "title": "Plot observed history, fitted values and forecast with uncertainty",
      "topics": [
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    },
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      "title": "Plot the fitted latent trajectory",
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      "title": "Dynamic Models for Poisson, Binomial and Multinomial Time Series",
      "author": "Gregor Zens",
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        "Simulating data",
        "Fitting a Poisson model",
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