Deterministic vs Probabilistic Model

本文深入探讨了确定性模型与概率模型的核心概念、特点及应用领域,揭示了两者在数学建模中的本质差异,帮助读者在面对随机现象时做出更明智的选择。

Deterministic  Model

A deterministic model is a mathematical model in which outcomes are precisely determined through known relationships among states and events, without room for random variation.


probabilistic model

A probability model is a mathematical representation of a random phenomenon, It is defined by it's sample, events within the sample space, and probabilities.

The models are also defined as statistical analysis tool that estimates, on the basis of past data, the probability of an event occurring. 

Unlike the deterministic model, the probabilistic model elements of randomness, This model is likely to produce different results even with the same initial conditions. There is always an element of chance or uncertainty involved which implies that there are possible alternate solutions.                                   

The probabilistic model includes both a deterministic component and a random error component.

[OccMap]: No prebuilt map found/not using the prebuilt map. /home/vivien/.local/lib/python3.8/site-packages/tensordict/nn/probabilistic.py:497: UserWarning: deterministic_sample wasn't found when queried on <class 'utils.IndependentBeta'>. SafeProbabilisticModule is falling back on mode instead. For better code quality and efficiency, make sure to either provide a distribution with a deterministic_sample attribute or to change the InteractionMode to the desired value. warnings.warn( /home/vivien/catkin_ws/src/try5/navigation_runner/scripts/utils.py:168: UserWarning: Using torch.cross without specifying the dim arg is deprecated. Please either pass the dim explicitly or simply use torch.linalg.cross. The default value of dim will change to agree with that of linalg.cross in a future release. (Triggered internally at ../aten/src/ATen/native/Cross.cpp:62.) goal_direction_y = torch.cross(z_direction.expand_as(goal_direction_x), goal_direction_x) /home/vivien/catkin_ws/src/try5/navigation_runner/scripts/navigation.py:105: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. policy.load_state_dict(torch.load(os.path.join(file_dir, checkpoint), map_location=self.cfg.device)) [Navigation] Press Enter to STOP motion! [INFO] [1763244081.521354, 61.407000]: [Navigation] Waiting for odom... [INFO] [1763244081.522558, 61.408000]: [Navigation] Ground robot ready!
最新发布
11-17
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