TY  -  
T1  - Bayesian model based spatiotemporal survey designs and partially observed log Gaussian Cox process 
SN  -  /  
UR  - http://hdl.handle.net/10138/312165 
T3  -  
A1  - Liu, Jia; Vanhatalo, Jarno 
A2  -  
PB  -  
Y1  - 2020 
LA  - eng 
AB  - In geostatistics, the spatiotemporal design for data collection is central for accurate prediction and parameter inference. An important class of geostatistical models is log-Gaussian Cox process (LGCP) but there are no formal analyses on spatial or spatiotemporal survey designs for them. In this work, we study traditional balanced and uniform random designs in situations where analyst has prior information on intensity function of LGCP and show that the traditional balanced and random designs a... 
VO  -  
IS  -  
SP  -  
OP  -  
KW  - 112 Statistics and probability; Experimental design; Bayesian inference; Kullback-Leibler information; Log Gaussian Cox process; Rejection sampling design; Species distribution; POINT PROCESS MODELS; PRESENCE-ONLY DATA; INFERENCE; INFORMATION; SPACE 
N1  -   
PP  -  
ER  -