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Chapter 8: Model Inference and Averaging

Chapter 8: Model Inference and Averaging

Chapter 8: Model Inference and Averaging. Presented by Hui Fang. Basic Concepts. Statistical inference Using data to infer the distribution that generated the data We observe . We want to infer (or estimate or learn) F or some feature of F such as its mean. Statistical model

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618 views • 34 slides
Chapter 8: Model Inference and Averaging

Chapter 8: Model Inference and Averaging

Chapter 8: Model Inference and Averaging. Presented by Hui Fang. Basic Concepts. Statistical inference Using data to infer the distribution that generated the data We observe . We want to infer (or estimate or learn) F or some feature of F such as its mean. Statistical model

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352 views • 34 slides
Goodness of Fit using Bootstrap

Goodness of Fit using Bootstrap

Goodness of Fit using Bootstrap. G. Jogesh Babu Center for Astrostatistics http://astrostatistics.psu.edu. Astrophysical Inference from astronomical data. Fitting astronomical data Non-linear regression Density (shape) estimation Parametric modeling Parameter estimation of assumed model

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383 views • 21 slides
Goodness of Fit using Bootstrap

Goodness of Fit using Bootstrap

Goodness of Fit using Bootstrap. G. Jogesh Babu Center for Astrostatistics http://astrostatistics.psu.edu. Astrophysical Inference from astronomical data. Fitting astronomical data Non-linear regression Density (shape) estimation Parametric modeling Parameter estimation of assumed model

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365 views • 21 slides
Goodness of Fit using Bootstrap

Goodness of Fit using Bootstrap

Goodness of Fit using Bootstrap. G. Jogesh Babu Center for Astrostatistics http://astrostatistics.psu.edu. Astrophysical Inference from astronomical data. Fitting astronomical data Non-linear regression Density (shape) estimation Parametric modeling Parameter estimation of assumed model

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212 views • 21 slides
Data Analysis with Bayesian Networks: A Bootstrap Approach

Data Analysis with Bayesian Networks: A Bootstrap Approach

Data Analysis with Bayesian Networks: A Bootstrap Approach. Nir Friedman, Moises Goldszmidt, and Abraham Wyner, UAI99. Abstract. Confidence on learned Bayesian networks Edges How can we believe that the presence of an edge is true? Markov blankets The Markov blanket of a variable is true?

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294 views • 15 slides
Limits to Statistical Theory Bootstrap analysis

Limits to Statistical Theory Bootstrap analysis

Limits to Statistical Theory Bootstrap analysis. ESM 206 11 April 2006. Assumption of t -test. Sample mean is a t -distributed random variable Guaranteed if observations are normally distributed random variables or sample size is very large

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178 views • 10 slides
Bootstrap - Example

Bootstrap - Example

Bootstrap - Example. Suppose we have an estimator of a parameter and we want to express its accuracy by its standard error but its sampling distribution is too complicated to derive theoretically.

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463 views • 6 slides

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