@@ -212,7 +212,7 @@ class TimeSeasonality(Component):
212212 sigma_level_trend = pm.HalfNormal(
213213 "sigma_level_trend", sigma=1e-6, dims=ss_mod.param_dims["sigma_level_trend"]
214214 )
215- coefs_annual = pm.Normal("coefs_annual ", sigma=1e-2, dims=ss_mod.param_dims["coefs_annual "])
215+ params_annual = pm.Normal("params_annual ", sigma=1e-2, dims=ss_mod.param_dims["params_annual "])
216216
217217 ss_mod.build_statespace_graph(data)
218218 idata = pm.sample(
@@ -298,10 +298,10 @@ def populate_component_properties(self):
298298 for endog_name in self .observed_state_names
299299 for state_name in self .provided_state_names
300300 ]
301- self .param_names = [f"coefs_ { self .name } " ]
301+ self .param_names = [f"params_ { self .name } " ]
302302
303303 self .param_info = {
304- f"coefs_ { self .name } " : {
304+ f"params_ { self .name } " : {
305305 "shape" : (k_states ,) if k_endog == 1 else (k_endog , k_states ),
306306 "constraints" : None ,
307307 "dims" : (f"state_{ self .name } " ,)
@@ -311,7 +311,7 @@ def populate_component_properties(self):
311311 }
312312
313313 self .param_dims = {
314- f"coefs_ { self .name } " : (f"state_{ self .name } " ,)
314+ f"params_ { self .name } " : (f"state_{ self .name } " ,)
315315 if k_endog == 1
316316 else (f"endog_{ self .name } " , f"state_{ self .name } " )
317317 }
@@ -378,7 +378,7 @@ def make_symbolic_graph(self) -> None:
378378 self .ssm ["design" , :, :] = pt .linalg .block_diag (* [Z for _ in range (k_endog )])
379379
380380 initial_states = self .make_and_register_variable (
381- f"coefs_ { self .name } " ,
381+ f"params_ { self .name } " ,
382382 shape = (k_unique_states ,) if k_endog == 1 else (k_endog , k_unique_states ),
383383 )
384384 if k_endog == 1 :
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