scholarly journals Parameter space analysis, pattern sensitivity and model comparison for Turing and stationary flow-distributed waves (FDS)

2001 ◽  
Vol 160 (1-2) ◽  
pp. 79-102 ◽  
Author(s):  
Razvan A Satnoianu ◽  
Philip K Maini ◽  
Michael Menzinger
Keyword(s):  
Parameter Space ◽  
Model Comparison ◽  
Stationary Flow ◽  
Space Analysis ◽  
Analysis Pattern ◽  
Pattern Sensitivity

Parameter Space Analysis Suggests Multi-Site Plasticity Contributes to Motor Pattern Initiation in Tritonia

2007 ◽  
Vol 98 (4) ◽  
pp. 2382-2398 ◽  
Author(s):  
Robert J. Calin-Jageman ◽  
Mark J. Tunstall ◽  
Brett D. Mensh ◽  
Paul S. Katz ◽  
William N. Frost
Keyword(s):  
Parameter Space ◽  
Large Scale ◽  
Time Course ◽  
Scale Parameter ◽  
Motor Pattern ◽  
Rhythmic Activity ◽  
Excitatory Input ◽  
Space Analysis ◽  
Gastropod Mollusk ◽  
Detailed Simulation

This research examines the mechanisms that initiate rhythmic activity in the episodic central pattern generator (CPG) underlying escape swimming in the gastropod mollusk Tritonia diomedea. Activation of the network is triggered by extrinsic excitatory input but also accompanied by intrinsic neuromodulation and the recruitment of additional excitation into the circuit. To examine how these factors influence circuit activation, a detailed simulation of the unmodulated CPG network was constructed from an extensive set of physiological measurements. In this model, extrinsic input alone is insufficient to initiate rhythmic activity, confirming that additional processes are involved in circuit activation. However, incorporating known neuromodulatory and polysynaptic effects into the model still failed to enable rhythmic activity, suggesting that additional circuit features are also required. To delineate the additional activation requirements, a large-scale parameter-space analysis was conducted (∼2 × 106 configurations). The results suggest that initiation of the swim motor pattern requires substantial reconfiguration at multiple sites within the network, especially to recruit ventral swim interneuron-B (VSI) activity and increase coupling between the dorsal swim interneurons (DSIs) and cerebral neuron 2 (C2) coupling. Within the parameter space examined, we observed a tendency for rhythmic activity to be spontaneous and self-sustaining. This suggests that initiation of episodic rhythmic activity may involve temporarily restructuring a nonrhythmic network into a persistent oscillator. In particular, the time course of neuromodulatory effects may control both activation and termination of rhythmic bursting.

scholarly journals Accelerated Bayesian inference using deep learning

2020 ◽  
Vol 496 (1) ◽  
pp. 328-338
Author(s):  
Adam Moss
Keyword(s):  
Bayesian Inference ◽  
Parameter Space ◽  
Model Comparison ◽  
Excellent Performance ◽  
Unit Variance ◽  
Standard Cosmological Model ◽  
High Degree ◽  
Parameter Constraints ◽  
Volume Preserving ◽  
Made In

ABSTRACT We present a novel Bayesian inference tool that uses a neural network (NN) to parametrize efficient Markov Chain Monte Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-linear, invertible, and non-volume preserving flows. NNs are extremely expressive, and can transform complex targets to a simple latent representation. Efficient proposals can then be made in this space, and we demonstrate a high degree of mixing on several challenging distributions. Parameter space can naturally be split into a block diagonal speed hierarchy, allowing for fast exploration of subspaces where it is inexpensive to evaluate the likelihood. Using this method, we develop a nested MCMC sampler to perform Bayesian inference and model comparison, finding excellent performance on highly curved and multimodal analytic likelihoods. We also test it on Planck 2015 data, showing accurate parameter constraints, and calculate the evidence for simple one-parameter extensions to the standard cosmological model in ∼20D parameter space. Our method has wide applicability to a range of problems in astronomy and cosmology and is available for download from https://github.com/adammoss/nnest.

Visual Parameter Space Analysis: A Conceptual Framework

2014 ◽  
Vol 20 (12) ◽  
pp. 2161-2170 ◽  
Author(s):  
Michael Sedlmair ◽  
Christoph Heinzl ◽  
Stefan Bruckner ◽  
Harald Piringer ◽  
Torsten Moller
Keyword(s):  
Conceptual Framework ◽  
Parameter Space ◽  
Space Analysis ◽  
Visual Parameter

scholarly journals Bayes factor vs. Posterior-Predictive Model Assessment: Insights from Ordinal Constraints

2021 ◽  
Author(s):  
Julia M. Haaf ◽  
Fayette Klaassen ◽  
Jeffrey N. Rouder
Keyword(s):  
Predictive Model ◽  
Parameter Space ◽  
Model Comparison ◽  
Bayes Factor ◽  
Research Question ◽  
Central Element ◽  
Model Specification ◽  
Theoretical Interpretation ◽  
Model Assessment ◽  
Predictive Methods

A central element of statistical inference is good model specification where researchers specify models that capture differing theoretical position. We argue that methods of inference forcing researchers to use models that may not be appropriate for their research question are not as desirable as methods with no such constraints. We ask how posterior-predictive model assessment methods such as wAIC and LOO-CV perform when theoretical positions correspond to different space restrictions on a common parameter space. One of the main theoretical relations is nesting — where the parameter space of one model is a subset of that for another. A good example is a general model that admits any set of preferences; a nested model is one that admits only preferences that obey transitivity. We find that posterior-predictive methods fail in these cases: More constrained models are not favored even when data are compatible with the constraint. Researchers who use posterior predictive methods are forced to partition the parameter space into non-overlapping subspaces, even if these subspaces have no theoretical interpretation. Fortunately, Bayes factor model comparison accommodates overlapping models without such difficulties. We argue given that posterior predictive approaches force certain specifications that may not be ideal for scientific questions, they are less desirable in many contexts.

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