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In particular, we noted the importance of understanding the status of theories and of appreciating that testing empirical hypotheses can involve complex inferences and methods.
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They also have great currency for Gassendi, who typically crafts his use of syllogism and causal inference and method after his Institutio conceptions, or at least signals that he intends to do so.
We developed a workflow to efficiently analyse information generated by different inference and prediction methods.
Three types of analyses were performed; Bayesian inference, and two methods in the maximum likelihood framework (PhyML, RAxML).
There were substantial differences in the cold spell definitions, reference periods, the basis of inference, and statistical methods.
We found that tree topologies were sensitive to different datasets and inference methods, and support values were higher in the BI tree than in the ML tree using the same dataset.
Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes.
Phylogenetic analysis based on maximum likelihood and Bayesian inference methods and the population splits test suggested and supported the independent origins of indica and japonica within Asian cultivated rice.
We performed phylogenetic analyses with four datasets (P12, P123, P12LRT, and P123RT) and two inference methods (BI and ML) (Fig. 8, Additional files 9, 10 and 11).
Based on these alignments phylogenetic trees were inferred using Bayesian Inference and Maximum Likelihood methods.
Then we propose the model learning and inference methods to discover the latent correlation between RGB and depth data as well as model temporal context within individual modality.
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