Hierarchical Bayesian Estimation
Hierarchical Bayesian Estimation - Related Videos
12:39
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Cited by 6 •
2012
Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.
01:06
What are Estimates?
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2023
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
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Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
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2019
This protocol demonstrates how to use the Auto-CHO software for hierarchical and programmable one-pot synthesis of oligosaccharides. It also describes the general procedure for RRV determination experiments and one-pot glycosylation of SSEA-4.
06:50
Synthesis of Hierarchical ZnO/CdSSe Heterostructure Nanotrees
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2016
Here, we prepare and characterize novel tree-like hierarchical ZnO/CdSSe nanostructures, where CdSSe branches are grown on vertically aligned ZnO nanowires. The resulting nanotrees are a potential material for solar energy conversion and other opto-electronic devices.
07:05
The Precision of Visual Working Memory with Delayed Estimation
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Human memory is limited. Throughout most of its history, experimental psychology has focused on investigating the discrete, quantitative limits of memory—how many individual pieces of information a person can remember. Recently, experimental psychologists have also become interested in more qualitative limits—how precisely is information stored? The concept of memory precision can be both intuitive and elusive at once. It is...
FAQs
Its central mechanism is partial pooling: individual-level estimates are informed by group-level distributions rather than treated as entirely separate. When a person’s behavioral data are sparse, group information helps stabilize that person’s estimate; when the data provide stronger individual evidence, the estimate can reflect it. This balances population structure with meaningful individual variation.
In Hierarchical Bayesian Estimation, priors express starting distributions for unknown parameters, the likelihood connects those parameters to observed behavioral data, and the posterior combines both sources. The posterior is not just a single estimate; it quantifies uncertainty around parameter values. This makes uncertainty part of the inference rather than an afterthought when interpreting behavioral effects.
Different levels answer different behavioral questions. Trial-level variation captures changes or noise within a person, whereas individual-level parameters describe differences between people; group-level distributions summarize how those individual parameters vary across the population. Keeping these levels distinct helps researchers avoid treating repeated observations as if they represented only one source of variation.
The framework can represent how behavioral processes vary across contexts by placing related estimates within a common multilevel structure. This allows researchers to examine population regularities while retaining differences among individuals and their decisions. Such a structure is useful when the scientific goal is not merely to predict responses, but to characterize how a process changes across people or settings.
A practical workflow starts by identifying the behavioral observations and their levels, such as trials within individuals and individuals within groups. Researchers then specify priors for unknown parameters and a likelihood for the observed data. Combining these quantities produces posterior estimates for the relevant levels, which can be examined with their uncertainty and used for prediction.
Repeated decisions provide observations across trials, while learning trajectories describe how behavior changes over time. Hierarchical Bayesian Estimation can incorporate these patterns alongside individual and group differences, allowing researchers to separate trial-level noise from more persistent behavioral variation. The resulting estimates support analysis of both how behavior unfolds within people and how those processes differ across individuals.
It is especially useful when researchers combine data from individuals and populations or when some individuals contribute relatively sparse observations. Group-level distributions can regularize individual estimates in those circumstances, while posterior uncertainty indicates how strongly the data support each result. The framework also supports principled predictions and comparisons of behavioral processes across contexts.