Nonparametric Bayes: A Bridge Between Cultures
نویسندگان
چکیده
منابع مشابه
Nonparametric Bayes-risk estimation
Absrract-Two nonparametric methods to estimate the Bayes risk using classified sample sets are described and compared. The first method uses the nearest neighbor error rate as an estimate to bound the Bayes risk. The second method estimates the Bayes decision regions by applying Parzen probability-density function estimates and counts errors made using these regions. This estimate is shown to b...
متن کاملNonparametric Bayes I
In finite mixture models, we know a priori the number K of clusters existing in the data. Each data point is generated by one of K distributions, each of which is characterized by some parameters. For example, we can cluster the data using K-means or Gaussian mixture models. These approaches are widely used in machine learning and statistics, and are applied in areas such as image processing, i...
متن کاملNonparametric Bayes Pachinko Allocation
Recent advances in topic models have explored complicated structured distributions to represent topic correlation. For example, the pachinko allocation model (PAM) captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). While PAM provides more flexibility and greater expressive power than previous models like latent Dirichlet allocation ...
متن کاملA stochastic programming perspective on nonparametric Bayes
We use Church, a Turing-universal language for stochastic generative processes and the probability distributions they induce, to study and extend several objects in nonparametric Bayesian statistics. We connect exchangeability and de Finetti measures with notions of purity and closures from functional programming. We exploit delayed evaluation to provide finite, machine-executable representatio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Observational studies
سال: 2021
ISSN: ['2767-3324']
DOI: https://doi.org/10.1353/obs.2021.0005