Bayesian Inferences

2021 ◽  
pp. 357-382
Author(s):  
Peter Müller
Keyword(s):  
Bayesian Inferences

Thinking in Bayesian inferences: A brief reflection on statistical approach for psychological research

2021 ◽  
Author(s):  
Tri Tam Le
Keyword(s):  
Bayesian Analysis ◽  
Personal Experience ◽  
Statistical Approach ◽  
Psychological Research ◽  
Analysis Method ◽  
Bayesian Inferences

I give some benefits of the Bayesian analysis method from my personal experience in psychological research.

scholarly journals Does Concealing a Sexual Minority Identity Prevent Exposure to Prejudice?

2019 ◽  
Vol 10 (8) ◽  
pp. 1056-1064 ◽  
Author(s):  
Jin X. Goh ◽  
Daniel N. Kort ◽  
Avery M. Thurston ◽  
Lina R. Benson ◽  
Cheryl R. Kaiser
Keyword(s):  
Sexual Minority ◽  
Sexual Minorities ◽  
Null Model ◽  
The Self ◽  
Protective Properties ◽  
Intergroup Interactions ◽  
Bayesian Inferences ◽  
Sexual Orientations ◽  
Potential Benefits ◽  
Benefits And Costs

Concealing a stigmatized identity is considered self-protective in that it presumably decreases exposure to bias during intergroup interactions relative to disclosing the identity. We conducted two studies exploring sexual minorities’ expectations about the self-protective properties of concealment and the reality concerning whether concealment prevents exposure to bias. In Study 1, half of sexual minorities who imagined interacting with a straight peer chose to conceal their identity, and this was predicted by the belief that concealment carries protective benefits. Study 2 randomly assigned sexual minorities to reveal or conceal their sexual orientations in actual interactions with straight peers. Neither sexual minority partners nor independent sexual minority coders perceived less bias among straight partners who interacted with sexual minorities concealing versus disclosing their identities. This was confirmed with Bayesian inferences demonstrating more evidence for the null model than the alternative. We discuss the potential benefits and costs of disclosure.

Amanita mansehraensis, a new species in section Vaginatae from Pakistan

Phytotaxa ◽  
2019 ◽  
Vol 409 (4) ◽  
pp. 189-201 ◽  
Author(s):  
MALKA SABA ◽  
DANNY HAELEWATERS ◽  
MUHAMMAD FIAZ ◽  
ABDUL NASIR KHALID ◽  
DONALD H. PFISTER
Keyword(s):  
New Species ◽  
Ribosomal Rna ◽  
Internal Transcribed Spacer ◽  
Sequence Data ◽  
Large Subunit ◽  
Pine Forests ◽  
Evolutionary Relationships ◽  
Internal Transcribed Spacer Region ◽  
Bayesian Inferences ◽  
A New Species

A new species of Amanita subgenus Amanita sect. Vaginatae is described and illustrated based on material collected in pine forests in district Mansehra, Khyber Pakhtoonkhaw, Pakistan. Amanita mansehraensis is recognized by the presence of a light brown or light greyish olive pileus with strong brown or deep brown pileus center; non-appendiculate, rimose, sulcate or plicate striate pileus margin; subglobose to ellipsoid basidiospores; and a saccate volva. The internal transcribed spacer region (ITS) and large subunit of the nuclear ribosomal RNA gene (nrLSU) were used for the delimitation of this species based on sequence data. The evolutionary relationships of A. mansehraensis with other species of Amanita were inferred by means of Maximum Likelihood and Bayesian inferences of the nrLSU dataset and concatenated ITS+nrLSU dataset. Amanita mansehraensis is most closely related to A. brunneofuliginea, A. pseudovaginata, and the recently described A. glarea.

Damage Identification for Masonry Materials Based on Bayesian Inference

2013 ◽  
Vol 405-408 ◽  
pp. 2498-2502
Author(s):  
Xiang Ping Fu ◽  
Bin Peng ◽  
Zheng Ji
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Sampling Method ◽  
Damage Identification ◽  
Dynamic Test ◽  
Finite Element Models ◽  
Masonry Structures ◽  
Dynamic Tests ◽  
Bayesian Inferences ◽  
Mcmc Sampling

The basic frequency of masonry specimens can be obtained by dynamic tests with ambient or artificial excitation. The elastic modulus of masonry structures, as well as the damage factors, can then be determined by training their finite element models and make the calculated frequencies agree with the measured ones. Using 530 groups of dynamic test data, the damage factors of four masonry specimens were identified. The Bayesian inferences of the highly diverse measured results were conducted through a Markov Chain Monte Carlo (MCMC) sampling method, and the location of the damage was identified. The methodology was applicable, and can be used in the damage identification for other materials or structures.

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