Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Bayesian inference approach in modeling and forecasting maize production in Rwanda

Creator:
DenDen
Publisher:
Sta
Host:

Visit

doi.org

Similar

Spatio-Temporal Forecasting of Global Horizontal Irradiance Using Bayesian InferenceHodal66/Crop-Price-Forecasting-in-Rwanda-using-Time-Series-ModelingModeling Spatial Heterogeneity in Exposure Buffers and Risk: A Hierarchical Bayesian ApproachModeling Mortality of Children Under Five in Ethiopia Using Bayesian ApproachComparative Production Modeling and Forecasting of Crude Oil and Natural Gas in NigeriaParameter inference and model selection in deterministic and stochastic dynamical models via approximate Bayesian computation: modeling a wildlife epidemic

Spatio-Temporal Forecasting of Global Horizontal Irradiance Using Bayesian Inference

Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of the r

Hodal66/Crop-Price-Forecasting-in-Rwanda-using-Time-Series-Modeling

Predicting future prices of staple crops like beans, maize, and bananas in Rwanda using WFP food pri

Modeling Spatial Heterogeneity in Exposure Buffers and Risk: A Hierarchical Bayesian Approach

Place-based epidemiology studies often rely on circular buffers to define ``exposure'' to spatially

Modeling Mortality of Children Under Five in Ethiopia Using Bayesian Approach

This study utilizes a Bayesian Semi-Parametric Discrete-Time Survival Model to analyze the determina

Comparative Production Modeling and Forecasting of Crude Oil and Natural Gas in Nigeria

Abstract Nigeria's petroleum sector continues to face challenges related to prod

Parameter inference and model selection in deterministic and stochastic dynamical models via approximate Bayesian computation: modeling a wildlife epidemic

We consider the problem of selecting deterministic or stochastic models for a biological, ecological